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QUASI-POISSON VS. NEGATIVE BINOMIAL REGRESSION: HOW SHOULD WE MODEL OVERDISPERSED COUNT DATA?

2007; Wiley; Volume: 88; Issue: 11 Linguagem: Inglês

10.1890/07-0043.1

1939-9170

Jay M. Ver Hoef, Peter L. Boveng,

Avian ecology and behavior

EcologyVolume 88, Issue 11 p. 2766-2772 Statistical Report QUASI-POISSON VS. NEGATIVE BINOMIAL REGRESSION: HOW SHOULD WE MODEL OVERDISPERSED COUNT DATA? Jay M. Ver Hoef, Corresponding Author Jay M. Ver Hoef [email protected] E-mail: [email protected]Search for more papers by this authorPeter L. Boveng, Peter L. Boveng National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, 7600 Sand Point Way NE, Building 4, Seattle, Washington 98115-6349 USASearch for more papers by this author Jay M. Ver Hoef, Corresponding Author Jay M. Ver Hoef [email protected] E-mail: [email protected]Search for more papers by this authorPeter L. Boveng, Peter L. Boveng National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, 7600 Sand Point Way NE, Building 4, Seattle, Washington 98115-6349 USASearch for more papers by this author First published: 01 November 2007 https://doi.org/10.1890/07-0043.1Citations: 687 Corresponding Editor: N. G. Yoccoz. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract Quasi-Poisson and negative binomial regression models have equal numbers of parameters, and either could be used for overdispersed count data. While they often give similar results, there can be striking differences in estimating the effects of covariates. We explain when and why such differences occur. The variance of a quasi-Poisson model is a linear function of the mean while the variance of a negative binomial model is a quadratic function of the mean. These variance relationships affect the weights in the iteratively weighted least-squares algorithm of fitting models to data. Because the variance is a function of the mean, large and small counts get weighted differently in quasi-Poisson and negative binomial regression. We provide an example using harbor seal counts from aerial surveys. These counts are affected by date, time of day, and time relative to low tide. 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