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From |
"Steichen, Thomas J." <SteichT@rjrt.com> |

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

Subject |
RE: st: Zero-inflated binomial regression |

Date |
Tue, 23 Oct 2007 11:19:17 -0400 |

There is also the zero-inflated negative binomial model (ZINB), which allows overdispersion (a variance that exceeds the mean) in the number of academic after-school programs. Maarten's commentary below would apply to this model also except that the "number of programs" woulds not need to be determined through a strict poisson (variance = mean) process. Tom ----------------------------------- Thomas J. Steichen steicht@rjrt.com ----------------------------------- -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis Sent: Tuesday, October 23, 2007 9:39 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Zero-inflated binomial regression --- Meryle Weinstein <mgw216@nyu.edu> wrote: > I have count data and have been doing analyses using negative > binomial regression. I've been doing reading and think that the > zero-inflated binomial regression may be more appropriate given the > number of zeros in data (243 out of 626). Two comments: 1) I assume you mean zero inflated poisson (-zip- in Stata) instead of zero-inflated binomial. 2) The negative binomial is also meant to deal with excessive zeros, although it assumes these came into existence through a different process. > The data is the count of academic after-school programs in an > elementary school zone. The zones could have zero because > they don't have any after-school programs (which is the majority of > cases) or zero because there are no academic programs. What > I don't understand and haven't been able to find in the readings is > how to choose the variables for inflate. With -zip- you assume that there are two types of districts, a type of district that will always have 0 programs, and a type of district whereby the number of programs is determined through a poisson regression (which may include 0 programs). You haven't observed the type, but only the count and this is a mixture of the two processes. The -inflate(varlist)- option tells -zip- which variables predict the type of district. So you choose those variables you think will influence the probability of being an "always zero program district". For more on this I highly recomend "Regression Models for Categorical Dependent Variables Using Stata" by J. Scott Long and Jeremy Freese. http://www.stata.com/bookstore/regmodcdvs.html Hope this helps, Maarten ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- ___________________________________________________________ Want ideas for reducing your carbon footprint? Visit Yahoo! For Good http://uk.promotions.yahoo.com/forgood/environment.html * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ ----------------------------------------- CONFIDENTIALITY NOTE: This e-mail message, including any attachment(s), contains information that may be confidential, protected by the attorney-client or other legal privileges, and/or proprietary non-public information. If you are not an intended recipient of this message or an authorized assistant to an intended recipient, please notify the sender by replying to this message and then delete it from your system. Use, dissemination, distribution, or reproduction of this message and/or any of its attachments (if any) by unintended recipients is not authorized and may be unlawful. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Zero-inflated binomial regression***From:*"Meryle Weinstein" <mgw216@nyu.edu>

**Re: st: Zero-inflated binomial regression***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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