Data Dispersion: Now You See it... Now You Don't

14 Pages Posted: 21 May 2010 Last revised: 25 Jul 2011

See all articles by Kimberly F. Sellers

Kimberly F. Sellers

Georgetown University - Department of Mathematics and Statistics

Galit Shmueli

Institute of Service Science, National Tsing Hua University, Taiwan

Date Written: May 21, 2010

Abstract

The most popular method for modeling count data is Poisson regression. When data display over-dispersion, thereby deeming Poisson regression inadequate, typically negative-binomial regression is instead used. We show that count data that appear to be equi-dispersed or over-dispersed may actually stem from a mixture of populations with different dispersion levels. To detect and model such a mixture, we introduce a generalization of the Conway-Maxwell-Poisson (COM-Poisson) regression that allows for group-level dispersion. We illustrate mixed dispersion effects and the proposed methodology via semi-authentic data.

Keywords: Conway-Maxwell-Poisson (COM-Poisson) regression, mixture model, negative binomial regression, over dispersion, under-dispersion

Suggested Citation

Sellers, Kimberly F. and Shmueli, Galit, Data Dispersion: Now You See it... Now You Don't (May 21, 2010). Robert H. Smith School Research Paper No. RHS 06-122. Available at SSRN: https://ssrn.com/abstract=1612755 or http://dx.doi.org/10.2139/ssrn.1612755

Kimberly F. Sellers

Georgetown University - Department of Mathematics and Statistics ( email )

United States
202-687-8829 (Phone)

HOME PAGE: http://www9.georgetown.edu/faculty/kfs7

Galit Shmueli (Contact Author)

Institute of Service Science, National Tsing Hua University, Taiwan ( email )

Hsinchu, 30013
Taiwan

HOME PAGE: http://www.iss.nthu.edu.tw

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