Objective Bayes testing of Poisson versus inflated Poisson models
M. J. Bayarri, James O. Berger, Gauri S. Datta
Abstract
The Poisson distribution is often used as a standard model for count data. Quite often, however, such data sets are not well fit by a Poisson model because they have more zeros than are compatible with this model. For these situations, a zero-inflated Poisson (ZIP) distribution is often proposed. This article addresses testing a Poisson versus a ZIP model, using Bayesian methodology based on suitable objective priors. Specific choices of objective priors are justified and their properties investigated. The methodology is extended to include covariates in regression models. Several applications are given.
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Primary Subjects: 62F15, 62F03
Keywords: Bayes factor; Jeffreys prior; model selection
Full-text: Open access
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Permanent link to this document: http://projecteuclid.org/euclid.imsc/1209398464
Digital Object Identifier: doi:10.1214/074921708000000093
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