Bivariate Conway-Maxwell-Poisson distribution: Formulation, properties, and inference

被引：22
|
作者：
Sellers, Kimberly F. [1 ,2 ]
Morris, Darcy Steeg [2 ]
Balakrishnan, Narayanaswamy [3 ]
机构：
[1] Georgetown Univ, Dept Math & Stat, Washington, DC 20057 USA
[2] US Bur Census, Ctr Stat Res & Methodol, Washington, DC 20233 USA
[3] McMaster Univ, Dept Math & Stat, Hamilton, ON L8S 4K1, Canada
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 2016年 / 150卷
基金：
加拿大自然科学与工程研究理事会;
关键词：
Bivariate distribution; Dispersion; Dependence; Conway-Maxwell-Poisson (COM-Poisson); FAMILY;
D O I：
10.1016/j.jmva.2016.04.007
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
The bivariate Poisson distribution is a popular distribution for modeling bivariate count data. Its basic assumptions and marginal equi-dispersion, however, may prove limiting in some contexts. To allow for data dispersion, we develop here a bivariate Conway-Maxwell-Poisson (COM-Poisson) distribution that includes the bivariate Poisson, bivariate Bernoulli, and bivariate geometric distributions all as special cases. As a result, the bivariate COM-Poisson distribution serves as a flexible alternative and unifying framework for modeling bivariate count data, especially in the presence of data dispersion. Published by Elsevier Inc.
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页码：152 / 168
页数：17
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