Bayesian Inference for Two-Parameter Gamma Distribution Assuming Different Noninformative Priors
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidad Nacional de Colombia
2013
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| _version_ | 1876438884450041856 |
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| author | Fernando Antonio Moala |
| author_facet | Fernando Antonio Moala |
| contents | Bayesian Inference for Two-Parameter Gamma Distribution Assuming Different Noninformative Priors Fernando Antonio Moala Pedro Luiz Ramos Jorge Alberto Achcar Física, Astronomía y Matemáticas MDIP MCMC copula conjugate reference 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. 2013 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89929799009 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.2 Vol.36 |
| format | Artículo científico |
| id | redalyc_89929799009 |
| institution | Redalyc |
| language | en |
| publishDate | 2013 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | Bayesian Inference for Two-Parameter Gamma Distribution Assuming Different Noninformative Priors Fernando Antonio Moala Física, Astronomía y Matemáticas MDIP MCMC copula conjugate reference Bayesian Inference for Two-Parameter Gamma Distribution Assuming Different Noninformative Priors Fernando Antonio Moala Pedro Luiz Ramos Jorge Alberto Achcar Física, Astronomía y Matemáticas MDIP MCMC copula conjugate reference 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. 2013 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89929799009 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.2 Vol.36 |
| title | Bayesian Inference for Two-Parameter Gamma Distribution Assuming Different Noninformative Priors |
| topic | Física, Astronomía y Matemáticas MDIP MCMC copula conjugate reference |
| url | https://www.redalyc.org/articulo.oa?id=89929799009 |