A flexible distribution class for count data

Kimberly F. Sellers (), Andrew W. Swift () and Kimberly S. Weems ()
Additional contact information
Kimberly F. Sellers: Georgetown University
Andrew W. Swift: University of Nebraska - Omaha
Kimberly S. Weems: North Carolina Central University

Journal of Statistical Distributions and Applications, 2017, vol. 4, issue 1, 1-21

Abstract: Abstract The Poisson, geometric and Bernoulli distributions are special cases of a flexible count distribution, namely the Conway-Maxwell-Poisson (CMP) distribution – a two-parameter generalization of the Poisson distribution that can accommodate data over- or under-dispersion. This work further generalizes the ideas of the CMP distribution by considering sums of CMP random variables to establish a flexible class of distributions that encompasses the Poisson, negative binomial, and binomial distributions as special cases. This sum-of-Conway-Maxwell-Poissons (sCMP) class captures the CMP and its special cases, as well as the classical negative binomial and binomial distributions. Through simulated and real data examples, we demonstrate this model’s flexibility, encompassing several classical distributions as well as other count data distributions containing significant data dispersion.

Keywords: Conway-Maxwell-Poisson (CMP); Negative binomial; Poisson; Binomial; Geometric; Bernoulli; Over-dispersion; Under-dispersion; 60E05; 62F10 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1186/s40488-017-0077-0

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