On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation

Detalhes bibliográficos
Autor(a) principal: Suzuki,A.K.
Data de Publicação: 2013
Outros Autores: Louzada,F., Cancho,V.G.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512013000300014
Resumo: In this paper we propose a bivariate long-term model based on the Farlie-Gumbel-Morgenstern copula to model, where the marginals are assumed to be long-term promotion time structured. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on artificial and real data.
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spelling On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computationBayesian approachcase deletion influence diagnosticscopula modelinglong-term survivalIn this paper we propose a bivariate long-term model based on the Farlie-Gumbel-Morgenstern copula to model, where the marginals are assumed to be long-term promotion time structured. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on artificial and real data.Sociedade Brasileira de Matemática Aplicada e Computacional2013-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512013000300014TEMA (São Carlos) v.14 n.3 2013reponame:TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)instname:Sociedade Brasileira de Matemática Aplicada e Computacionalinstacron:SBMAC10.5540/tema.2013.014.03.0441info:eu-repo/semantics/openAccessSuzuki,A.K.Louzada,F.Cancho,V.G.eng2014-03-07T00:00:00Zoai:scielo:S2179-84512013000300014Revistahttp://www.scielo.br/temaPUBhttps://old.scielo.br/oai/scielo-oai.phpcastelo@icmc.usp.br2179-84511677-1966opendoar:2014-03-07T00:00TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) - Sociedade Brasileira de Matemática Aplicada e Computacionalfalse
dc.title.none.fl_str_mv On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation
title On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation
spellingShingle On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation
Suzuki,A.K.
Bayesian approach
case deletion influence diagnostics
copula modeling
long-term survival
title_short On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation
title_full On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation
title_fullStr On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation
title_full_unstemmed On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation
title_sort On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation
author Suzuki,A.K.
author_facet Suzuki,A.K.
Louzada,F.
Cancho,V.G.
author_role author
author2 Louzada,F.
Cancho,V.G.
author2_role author
author
dc.contributor.author.fl_str_mv Suzuki,A.K.
Louzada,F.
Cancho,V.G.
dc.subject.por.fl_str_mv Bayesian approach
case deletion influence diagnostics
copula modeling
long-term survival
topic Bayesian approach
case deletion influence diagnostics
copula modeling
long-term survival
description In this paper we propose a bivariate long-term model based on the Farlie-Gumbel-Morgenstern copula to model, where the marginals are assumed to be long-term promotion time structured. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on artificial and real data.
publishDate 2013
dc.date.none.fl_str_mv 2013-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512013000300014
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512013000300014
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.5540/tema.2013.014.03.0441
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Matemática Aplicada e Computacional
publisher.none.fl_str_mv Sociedade Brasileira de Matemática Aplicada e Computacional
dc.source.none.fl_str_mv TEMA (São Carlos) v.14 n.3 2013
reponame:TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)
instname:Sociedade Brasileira de Matemática Aplicada e Computacional
instacron:SBMAC
instname_str Sociedade Brasileira de Matemática Aplicada e Computacional
instacron_str SBMAC
institution SBMAC
reponame_str TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)
collection TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)
repository.name.fl_str_mv TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) - Sociedade Brasileira de Matemática Aplicada e Computacional
repository.mail.fl_str_mv castelo@icmc.usp.br
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