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From |
jverkuilen <jverkuilen@gc.cuny.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: RE: Dependent var is a proportion, with large spike in .95+ |

Date |
Thu, 4 Sep 2008 23:36:37 -0400 |

The inverse logit-beta distribution turns out to be kind of interesting. I have a Mathematica notebook of the transformation if anyone cares. It is only symmetric when the beta is symmetric and starts to resemble (graphically---I did no formal proof... yet) extreme value distributions when the beta is skewed. There may be an advantage for estimation for using the IL-Beta, however. -----Original Message----- From: "Verkuilen, Jay" <JVerkuilen@gc.cuny.edu> To: statalist@hsphsun2.harvard.edu Sent: 9/4/2008 5:46 PM Subject: RE: st: RE: Dependent var is a proportion, with large spike in .95+ Maarten buis wrote: >Good point, a beta distribution can be skewed. To find out if your data >is beta distributed you can use -hangroot-, -pbeta-, and/or -qbeta-, >all downloadable from SSC. Type -findit hangroot-, -findit pbeta-, >and/or -findit qbeta- to find them. These are useful but remember that the regression model assumes that the response variable is *conditionally* beta. The marginal distribution without regressors can be markedly different from a beta as Y|X ~ Beta !=> Y ~ Beta. I am very interested in the point Nick raised about logit transforming the response. This is implicit in the ML equations because the sufficient statistics are log(y) and log(1-y) and it's easy to rewrite things in terms of log(y/(1-y)). I need to look at the distribution of the inverse logit-beta. I suspect it'll line up nicely with the exponential-gamma distribution. JV * * 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/ * * 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/

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