Title | A Flexible Regression Model for Count Data |
Publication Type | Journal Article |
Year of Publication | 2010 |
Authors | Sellers, K. F., and G. Shmueli |
Journal | Annals of Applied Statistics |
Volume | 4 |
Issue | 2 |
Pages | 943-961 |
Abstract | 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 (CMP) distribution to address this problem. The CMP regression generalizes the well-known Poisson and logistic regression models, and is suitable for tting 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 CMP regression over a standard Poisson regression. We compare the CMP to several alternatives and illustrate its advantages and usefulness using three datasets with varying dispersion. |
Notes | Attached are both the paper and the supplementary materials (two separate files). R code is available in the COMPoissonReg R package at http://cran.r-project.org/web/packages/COMPoissonReg/index.html |
URL | http://projecteuclid.org/euclid.aoas/1280842147 |
Attachment | Size |
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Supplementary Materials | 126.65 KB |
AOAS COM-Regression.pdf | 405.74 KB |