Inferencia bayesiana para la distribución gamma de dos parámetros asumiendo diferentes a prioris no informativas
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808129362402213888 |