{smcl}
{* 14dec2006}{...}
{hline}
help for {hi:invgausscf}
{hline}
{title:Fitting a two-parameter inverse Gaussian distribution by maximum likelihood (closed form)}
{p 8 17 2}
{cmd:invgausscf}
{it:varname}
[{it:weight}]
[{cmd:if} {it:exp}]
[{cmd:in} {it:range}]
{p 4 4 2}{cmd:by} {it:...} {cmd::} may be used with
{cmd:invgausscf}; see help {help by}.
{p 4 4 2}{cmd:fweight}s and {cmd:aweight}s are allowed;
see help {help weights}.
{title:Description}
{p 4 4 2} {cmd:invgausscf} fits by maximum likelihood a two-parameter
inverse Gaussian distribution to a distribution of a variable
{it:varname}. The distribution has probability density function for
variable x > 0, location parameter m > 0 and scale parameter
l > 0 of {bind:(l / 2 pi x^3)^(1/2) exp((-l (x - m)^2 / 2 m^2 x))}.
For a distribution without covariates the maximum likelihood solution
can be obtained directly from closed form solutions.
{title:Remarks}
{p 4 4 2}This program is provided in case it is interesting or useful.
{help invgaussfit} provides a fuller version, including fitting with
covariates and inferential results. Its help file also comments on
other commands available that use inverse Gaussian distributions in
some way.
{title:Saved results}
{p 4 4 2}{cmd:r(mu)} and {cmd:r(lambda)} are the estimated inverse
Gaussian parameters.
{title:Examples}
{p 4 8 2}{cmd:. invgausscf mpg}
{title:Author}
{p 4 4 2}Nicholas J. Cox, Durham University{break}n.j.cox@durham.ac.uk
{title:References}
{p 4 8 2}
Evans, M., Hastings, N. and Peacock, B. 2000. {it:Statistical distributions.}
New York: John Wiley.
{p 4 8 2}
Johnson, N.L., Kotz, S. and Balakrishnan, N. 1994.
{it:Continuous univariate distributions: Volume 1.} New York: John Wiley.
{title:Also see}
{p 4 13 2}
Online: help for
{help pinvgauss} (if installed),
{help qinvgauss} (if installed)
{help invgaussfit} (if installed)