[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CKSS.2004.11.2.381

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)

Publication Information

Communications for Statistical Applications and Methods / v.11, no.2, 2004 , pp. 381-397 More about this Journal

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;

Citations & Related Records

Reference

1	Bayes factors / [ Kass, R. E.;Raftery, A. E. ] / Journal of the American Statistical Association DOI ScienceOn
2	Simulating ratios of normalizing constants via a simple identity: a theoretical exploration / [ Meng, X. -L.;Wong, W. H. ] / Statistica Sinica
3	Bayesian generalized linear models for inference about small areas / [ Nandram, B.;D. K. Dey(ed.);S. K. Ghosh(ed.);B. K. Mallick(ed.) ] / Generalized linear models: A Bayesian Perspective
4	Marginal likelihood for a class of bayesian generalized linear models / [ Nandram, B.;Kim, H. ] / Journal of Statistical Computation and Simulation DOI ScienceOn
5	On Monte Carlo methods for estimating ratios of normalizing constants / [ Chen, M.-H.;Shao, Q.-M. ] / Annals of Statistics
6	Marginal likelihood from the Gibbs output / [ Chib, S. ] / Journal of the American Statistical Association DOI ScienceOn
7	Marginal likelihood from the Metropolis-Hastings output / [ Chib, S.;Jeliazkov, I. ] / Journal of the American Statistical Association DOI ScienceOn
8	Computing Bayes factors by combining simulation and asymptotic approximations / [ DiCiccio, T. J.;Kass, R. E.;Raftery, A. E.;Wasserman, L. ] / Journal of the American Statistical Association DOI ScienceOn
9	Model choice: Minimum posterior predictive loss approach / [ Gelfand, A.;Ghosh, S. ] / Biometrika DOI ScienceOn
10	Bayesian model choice: asymptotics and exact calculations / [ Gelfand,A.;Dey,D.K. ] / Journal of the Royal Statical Society
11	Efficient parameterizations for normal linear mixed models / [ Gelfand, A.;Sahu, S.;Carlin, B. ] / Biometrika DOI ScienceOn
12	Markov chain Monte Carlo methods for computing Bayes factors: A comparative review / [ Han, C.;Carlin, B. P. ] / Journal of the American Statistical Association DOI ScienceOn
13	/ [ Silverman,B. ] / Density Estimation for Statistics and Data Analysis
14	Computing Bayes factors using a generalization of the Savage-Dickey density ratio / [ Verdinelli,I.;Wasserman,L. ] / Journal of the American Statistical Association DOI ScienceOn
15	Hierarchical spatio-temporal mapping of disease rates / [ Waller, L.;Carlin, B.;Xia, H.;Gelfand, A. ] / Journal of the American Statistical Association DOI ScienceOn