Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Marginal Likelihoods for Bayesian Poisson Regression Models

  • Kim, Hyun-Joong (Department of Applied Statistics, Yonsei University) ;
  • Balgobin Nandram (Department of Mathematical Sciences, Worcester Polytechnic Institut) ;
  • Kim, Seong-Jun (Depatment of Industrial Engineering, kangnung National University) ;
  • Choi, Il-Su (Department of Applied Mathematics, Yosu National University) ;
  • Ahn, Yun-Kee (Department of Applied Statistics, Yonsei University) ;
  • Kim, Chul-Eung (Department of Applied Statistics, Yonsei University)
  • Published : 2004.08.01
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Abstract

The marginal likelihood has become an important tool for model selection in Bayesian analysis because it can be used to rank the models. We discuss the marginal likelihood for Poisson regression models that are potentially useful in small area estimation. Computation in these models is intensive and it requires an implementation of Markov chain Monte Carlo (MCMC) methods. Using importance sampling and multivariate density estimation, we demonstrate a computation of the marginal likelihood through an output analysis from an MCMC sampler.

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