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Bayesian Generalized Linear Model for Difference of Over or Under Dispersed Counts

Abstract Number:

3609 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Andrew Swift (1), Kimberly Sellers (2)

Institutions:

(1) University of Nebraska At Omaha, N/A, (2) North Carolina State University, N/A

Co-Author:

Kimberly Sellers  
North Carolina State University

First Author:

Andrew Swift  
University of Nebraska At Omaha

Presenting Author:

Andrew Swift  
University of Nebraska At Omaha

Abstract Text:

Modelling the difference of two counts has many practical uses in statistics. The Skellam distribution can be used for such a model, however since the Skellam distribution is constructed as the difference of two Poisson distributions it is potentially unsuitable for modelling data that suffers from under or over dispersion. We take a first look at constructing a Bayesian generalized linear model for the difference of counts that can handle both under and over dispersion based on the difference of two Conway-Maxwell Poisson distribution (that is, a Conway-Maxwell Skellam distribution). The focus of this paper is on providing an explicit demonstration using the Metropolis-Hastings algorithm.

Keywords:

Count Data|Overdispersion|Underdispersion|Conway-Maxwell Skellam|Bayesian|Metropolis-Hastings

Sponsors:

IMS

Tracks:

Statistical Methodology

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