Objective Bayes testing of Poisson versus inflated Poisson models

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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.
Published in at http://dx.doi.org/10.1214/074921708000000093 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Statistics Theory, Methodology, 62F15, 62F03 (Primary)

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http://salesiana.dossiersoluciones.com/handle/123456789/212645

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0805.3220
 http://arxiv.org/abs/0805.3220
 IMS Collections 2008, Vol. 3, 105-121
 doi:10.1214/074921708000000093

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arXiv