{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)