A FLEXIBLE REGRESSION MODEL FOR COUNT DATA

作者：Sellers Kimberly F^*; Shmueli Galit

来源：Annals of Applied Statistics, 2010, 4(2): 943-961.

DOI：10.1214/09-AOAS306

• Conway-Maxwell-Poisson (COM-Poisson) distribution
• dispersion
• generalized linear models (GLM)
• generalized Poisson

Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often over- or under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model based on the Conway Maxwell-Poisson (COM-Poisson) distribution to address this problem. The COM-Poisson regression generalizes the well-known Poisson and logistic regression models, and is suitable for fitting count data with a wide range of dispersion levels. With a GLM approach that takes advantage of exponential family properties, we discuss model estimation, inference, diagnostics, and interpretation, and present a test for determining the need for a COM-Poisson regression over a standard Poisson regression. We compare the COM-Poisson to several alternatives and illustrate its advantages and usefulness using three data sets with varying dispersion.

• 出版日期2010-6