Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas

Detalhes bibliográficos
Autor(a) principal: Antonio Moala, Fernando [UNESP]
Data de Publicação: 2013
Outros Autores: Luiz Ramos, Pedro [UNESP], Alberto Achcar, Jorge
Tipo de documento: Artigo
Idioma: eng
spa
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/220000
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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spelling Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativasBayesian inference for two-parameter gamma distribution assuming different noninformative priorsConjugateCopulaGamma distributionJeffreys priorMCMCMDIPNoninformative priorOrthogonalReferenceIn 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.Departamento de Estadística Universidade Estadual Paulista, Presidente PrudenteDepartamento de Medicina Social Universidade de São Paulo, Ribeirão PretoDepartamento de Estadística Universidade Estadual Paulista, Presidente PrudenteUniversidade Estadual Paulista (UNESP)Universidade de São Paulo (USP)Antonio Moala, Fernando [UNESP]Luiz Ramos, Pedro [UNESP]Alberto Achcar, Jorge2022-04-28T18:59:08Z2022-04-28T18:59:08Z2013-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article321-338Revista Colombiana de Estadistica, v. 36, n. 2, p. 321-338, 2013.0120-1751http://hdl.handle.net/11449/2200002-s2.0-84890880473Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengspaRevista Colombiana de Estadisticainfo:eu-repo/semantics/openAccess2022-04-28T18:59:08Zoai:repositorio.unesp.br:11449/220000Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:49:27.714848Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas
Bayesian inference for two-parameter gamma distribution assuming different noninformative priors
title Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas
spellingShingle Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas
Antonio Moala, Fernando [UNESP]
Conjugate
Copula
Gamma distribution
Jeffreys prior
MCMC
MDIP
Noninformative prior
Orthogonal
Reference
title_short Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas
title_full Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas
title_fullStr Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas
title_full_unstemmed Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas
title_sort Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas
author Antonio Moala, Fernando [UNESP]
author_facet Antonio Moala, Fernando [UNESP]
Luiz Ramos, Pedro [UNESP]
Alberto Achcar, Jorge
author_role author
author2 Luiz Ramos, Pedro [UNESP]
Alberto Achcar, Jorge
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv Antonio Moala, Fernando [UNESP]
Luiz Ramos, Pedro [UNESP]
Alberto Achcar, Jorge
dc.subject.por.fl_str_mv Conjugate
Copula
Gamma distribution
Jeffreys prior
MCMC
MDIP
Noninformative prior
Orthogonal
Reference
topic Conjugate
Copula
Gamma distribution
Jeffreys prior
MCMC
MDIP
Noninformative prior
Orthogonal
Reference
description 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.
publishDate 2013
dc.date.none.fl_str_mv 2013-12-01
2022-04-28T18:59:08Z
2022-04-28T18:59:08Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Revista Colombiana de Estadistica, v. 36, n. 2, p. 321-338, 2013.
0120-1751
http://hdl.handle.net/11449/220000
2-s2.0-84890880473
identifier_str_mv Revista Colombiana de Estadistica, v. 36, n. 2, p. 321-338, 2013.
0120-1751
2-s2.0-84890880473
url http://hdl.handle.net/11449/220000
dc.language.iso.fl_str_mv eng
spa
language eng
spa
dc.relation.none.fl_str_mv Revista Colombiana de Estadistica
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 321-338
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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