Special Issue Paper
The COM-Poisson model for count data: a survey of methods and applications
Article first published online: 8 SEP 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Applied Stochastic Models in Business and Industry
Special Issue: Statistics in Quality, Industry and Technology
Volume 28, Issue 2, pages 104–116, March/April 2012
How to Cite
Sellers, K. F., Borle, S. and Shmueli, G. (2012), The COM-Poisson model for count data: a survey of methods and applications. Appl. Stochastic Models Bus. Ind., 28: 104–116. doi: 10.1002/asmb.918
- Issue published online: 10 APR 2012
- Article first published online: 8 SEP 2011
- Manuscript Accepted: 7 JUN 2011
- Manuscript Revised: 13 APR 2011
- Manuscript Received: 6 DEC 2010
- regression model;
The Poisson distribution is a popular distribution for modeling count data, yet it is constrained by its equidispersion assumption, making it less than ideal for modeling real data that often exhibit over-dispersion or under-dispersion. The COM-Poisson distribution is a two-parameter generalization of the Poisson distribution that allows for a wide range of over-dispersion and under-dispersion. It not only generalizes the Poisson distribution but also contains the Bernoulli and geometric distributions as special cases. This distribution's flexibility and special properties have prompted a fast growth of methodological and applied research in various fields. This paper surveys the different COM-Poisson models that have been published thus far and their applications in areas including marketing, transportation, and biology, among others. Copyright © 2011 John Wiley & Sons, Ltd.