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Activity Number:
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444
- Recent Advances in Statistical Methodology for Big Data
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
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Sponsor:
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IMS
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Abstract #318384
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Title:
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Bayesian Generalized Linear Model for Difference of Over or Under Dispersed Counts
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Author(s):
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Andrew W Swift* and Kimberly Sellers
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Companies:
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University of Nebraska at Omaha and Georgetown University / U.S. Census Bureau
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Keywords:
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Count Data;
Overdispersion;
Underdispersion;
Conway-Maxwell Skellam;
Bayesian;
Metropolis-Hastings
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
