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From |
"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu> |

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

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

Date |
Wed, 3 Sep 2008 17:43:25 -0400 |

Dan Weitzenfeld wrote: >>That describes my situation exactly: I have a marked spike in my histogram at the top bin, roughly .95 - 1. I am wondering how to account for this.>> I am working on a model that combines zero inflation and a beta regression, essentially a combination of a beta regression for the continuous part and a logistic (or probit) for the boundary. It's not done in Stata (yet... but don't hold your breath). So far we've found it to be fairly tricky to implement--as zero inflation models tend to be---but it does work. Also, depending on the nature of your DV, there is little harm in "cheating" your observations away from 0 by using the transformation: Y_new = eps/2 + (1 - eps/2)*Y_old where eps > 0 is a small constant, e.g., .001. The beta likelihood is relatively insensitive to such perturbations (while other likelihoods are not). IMO, the real question is the nature of the zeros, as a recent post by Nick Cox makes plain. If the zero is a "real" one and means that there's something qualitatively different than something slightly less than 0 then you need an inflation model. If not, cheating often works. Whoops on rereading I see you have a sampling one. Well, same idea. 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/

**References**:**st: Dependent var is a proportion, with large spike in .95+***From:*"Dan Weitzenfeld" <dan.weitzenfeld@emsense.com>

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