Statistics Colloquium/PhD Defense
November 6 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

Poisson versus negative binomial regression in the analysis of count data

Barbie Bugna
Department of Statistics
Western Michigan University

Commonly used tests for treatment effect in kx2 frequency data are Poisson regression, negative binomial regression, and Cochran-Mantel-Haentzel. In practice, either Poisson regression or CMH is used as default, and NB regression is used only when there is reason to believe the data has overdispersion beyond what is expected of Poisson counts.

We show that the Poisson regression is sensitive to the Poisson assumption, and does not maintain its size in the presence of overdispersion. In particular, it tends to interpret overdispersion as significant treatment effect. Thus there is a need for a reliable pretest for the Poisson assumption. A commonly used diagnostic for overdispersion is a Wald test of the estimated overdispersion parameter, however this has convergence problems. We propose a simpler Hogg-type diagnostic that has no convergence problems and is easy to compute.

All statistics students are expected to attend.


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Department of Statistics
3304 Everett Tower
Western Michigan University
Kalamazoo MI 49008-5152 USA
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