Bayesian inference for two-parameter gamma distribution assuming different noninformative priors
In this paper distinct prior distributions are derived in a Bayesian inference of the two-parameters Gamma distribution. Noniformative priors, such as Jeffreys, reference, MDIP, Tibshirani and an innovative prior based on the copula approach are investigated. We show that the maximal data informatio...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2013 |
| País: | Brasil |
| Recursos: | Universidade Estadual Paulista (UNESP) |
| Repositório: | Repositório Institucional da UNESP |
| Idioma: | inglês |
| OAI Identifier: | oai:repositorio.unesp.br:11449/112051 |
| Acesso em linha: | http://revistas.unal.edu.co/index.php/estad/article/view/44351 http://hdl.handle.net/11449/112051 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Gamma distribution noninformative prior copula conjugate Jeffreys prior reference MDIP orthogonal MCMC |
| Resumo: | In this paper distinct prior distributions are derived in a Bayesian inference of the two-parameters Gamma distribution. Noniformative priors, such as Jeffreys, reference, MDIP, Tibshirani and an innovative prior based on the copula approach are investigated. We show that the maximal data information prior provides in an improper posterior density and that the different choices of the parameter of interest lead to different reference priors in this case. Based on the simulated data sets, the Bayesian estimates and credible intervals for the unknown parameters are computed and the performance of the prior distributions are evaluated. The Bayesian analysis is conducted using the Markov Chain Monte Carlo (MCMC) methods to generate samples from the posterior distributions under the above priors. |
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