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

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• 作者：Kimberly F. Sellers^a ; ^b ; ¹ ; ^{kfs7@georgetown.edu" class="auth_mail" title="E-mail the corresponding author} ; Darcy Steeg Morris^b ; ¹ ; ^{darcy.steeg.morris@census.gov" class="auth_mail" title="E-mail the corresponding author} ; Narayanaswamy Balakrishnan^c ; ^{bala@mcmaster.ca" class="auth_mail" title="E-mail the corresponding author}
• 关键词：60E05 ; 62E15 ; 62H12 ; 62-07 ; 62F10 ; 62F30 ; 62F03
• 刊名：Journal of Multivariate Analysis
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：150
• 期：Complete
• 页码：152-168
• 全文大小：508 K

文摘

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.