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

Sellers, K F and Shmueli, G (2010) Data Dispersion: Now You See it... Now You Don't. Working Paper. Indian School of Business, Hyderabad.

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.

ISB Creators:	ISB Creators ORCiD Shmueli, G UNSPECIFIED
Item Type:	Monograph (Working Paper)
Uncontrolled Keywords:	Conway-Maxwell-Poisson (COM-Poisson) regression, mixture model, negative binomial regression, over dispersion, under-dispersion
Subjects:	Business and Management Applied Statistics and Computing
Depositing User:	Veeramani R
Date Deposited:	16 Nov 2014 21:42
Last Modified:	20 Jan 2015 10:59
URI:	http://eprints.exchange.isb.edu/id/eprint/296
Publisher URL:	http://www.isb.edu/
Related URLs:	Link to full-text (other repositories) Google Scholar

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