On estimation and influence diagnostics for a bivariate promotion lifetime model based on the FGM copula: a fully bayesian computation
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , |
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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TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) |
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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 |
_version_ |
1752122219785355264 |