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From |
David Hoaglin <dchoaglin@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point |

Date |
Fri, 20 Jul 2012 12:44:51 -0400 |

Nick, You're correct that, in general, the g-and-h distributions do not have closed-form densities or cumulative distribution functions. The quantile function doesn't exist in closed form either, but only because the quantile function of the normal distribution is not closed-form. For reasons of resistance and robustness, I usually prefer to work with quantiles. Fitting by maximum likelihood opens you up to problems when the distribution has heavy tails and the data may contain outliers. Nowadays, fitting a g-and-h distribution by maximum likelihood is not a major problem, but it is not just a few lines of code! I don't know how much has been done on fitting models that involve predictors. In any event, the g-and-h distributions are a valuable part of my toolkit, but not a panacea. I have no basic problem with maximum likelihood. I've made heavy use of it, in Stata and elsewhere. But good data analysis is iterative: one should look at data and residuals at various stages. David Hoaglin On Fri, Jul 20, 2012 at 10:29 AM, Nick Cox <n.j.cox@durham.ac.uk> wrote: > Fair question for me at the end. I mean that g- and h- distributions are despite their flexibility rather awkward or elusive customers. It may be just psychology or convenience, but I like distributions with relatively simple closed-form definitions of density, distribution and quantile functions so that I can write a few lines of code to fit them by maximum likelihood, etc. Correct me if I am wrong, but g- and h- don't score well under that heading. As David implies, the practical problem is usually fitting a distribution given predictors, and fitting easily into the ML framework is to me highly desirable. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*Nick Cox <n.j.cox@durham.ac.uk>

**References**:**st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*Lucia Latino <Latino@economia.uniroma2.it>

**Re: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*Nick Cox <njcoxstata@gmail.com>

**R: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*"Lucia R.Latino" <Latino@economia.uniroma2.it>

**Re: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*Nick Cox <njcoxstata@gmail.com>

**R: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*"Lucia R.Latino" <Latino@economia.uniroma2.it>

**Re: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*David Hoaglin <dchoaglin@gmail.com>

**R: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*"Lucia R.Latino" <Latino@economia.uniroma2.it>

**Re: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*David Hoaglin <dchoaglin@gmail.com>

**RE: st: q-q plots, theoretical distribution with values higher than the sample's cutoff point***From:*Nick Cox <n.j.cox@durham.ac.uk>

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