Quasi-Poisson vs. Negative Binomial Regression: How Should We Model Overdispersed Count Data?
Quasi-Poisson vs. Negative Binomial Regression: How Should We Model Overdispersed Count Data?
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Washington, DC: Ecological Society of America
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Language
English
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Publisher
Washington, DC: Ecological Society of America
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Contents
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 li...
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Quasi-Poisson vs. Negative Binomial Regression: How Should We Model Overdispersed Count Data?
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TN_cdi_proquest_miscellaneous_69012679
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_69012679
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ISSN
0012-9658
E-ISSN
1939-9170
DOI
10.1890/07-0043.1
