Parametric and nonparametric Bayesian model specification: A case study involving models for count data |
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Authors: | Milovan Krnjaji? Athanasios Kottas |
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Affiliation: | a Lawrence Livermore National Laboratory, P.O. Box 808, L-227, Livermore, CA 94551, USA b Department of Applied Mathematics and Statistics, 1156 High Street, University of California, Santa Cruz, CA 95064, USA |
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Abstract: | In this paper we present the results of a simulation study to explore the ability of Bayesian parametric and nonparametric models to provide an adequate fit to count data of the type that would routinely be analyzed parametrically either through fixed-effects or random-effects Poisson models. The context of the study is a randomized controlled trial with two groups (treatment and control). Our nonparametric approach uses several modeling formulations based on Dirichlet process priors. We find that the nonparametric models are able to flexibly adapt to the data, to offer rich posterior inference, and to provide, in a variety of settings, more accurate predictive inference than parametric models. |
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Keywords: | Dirichlet process mixture model Markov chain Monte Carlo methods Random-effects Poisson model Stochastically ordered distributions |
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