Rejoinder: The COM-Poisson Model for count data: A survey of methods and applications

被引：0

作者：

Sellers, Kimberly F. ^{[1
]}

Borle, Sharad ^{[2
]}

Shmueli, Galit ^{[3
]}

机构：

[1] Georgetown Univ, Dept Math & Stat, Washington, DC 20057 USA

[2] Rice Univ, Jones Grad Sch Business, Dept Mkt, Houston, TX 77005 USA

[3] Indian Sch Business, Hyderabad 500032, Andhra Pradesh, India

来源：

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY | 2012年 / 28卷 / 02期

关键词：

biology; Conway-Maxwell-Poisson; marketing; overdispersion; regression model; transportation; underdispersion;

D O I：

10.1002/asmb.1923

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

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 (c) 2011 John Wiley & Sons, Ltd.

引用

页码：128 / 129

页数：2

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