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Activity Number: 61
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Government Statistics Section
Abstract #318974
Title: Introducing the Bivariate Conway-Maxwell-Poisson Distribution
Author(s): Kimberly Sellers* and Darcy S. Morris and Narayanaswamy Balakrishnan
Companies: Georgetown University and U.S. Census Bureau and McMaster University
Keywords: count data ; data dispersion ; over-dispersion ; under-dispersion
Abstract:

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 introduce 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. Presentation stems from work conducted with Darcy S. Morris (U.S. Census Bureau) and N. Balakrishnan (McMaster University).


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