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Marginal Likelihoods for Bayesian Poisson Regression Models
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 Title & Authors
Marginal Likelihoods for Bayesian Poisson Regression Models
Kim, Hyun-Joong; Balgobin Nandram; Kim, Seong-Jun; Choi, Il-Su; Ahn, Yun-Kee; Kim, Chul-Eung;
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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.
 Keywords
Poisson regression;Metropolis-Hastings sampler;multivariate density estimation;importance sampler;
 Language
English
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