Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3233/MAS-190477 http://hdl.handle.net/11449/221456 |
Resumo: | In this paper we propose a new cure rate survival model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes which may be responsible for the occurrence of the event of interest is assumed to follow a geometric distribution while the time to event is assumed to follow a Birnbaum-Saunders distribution. As an advantage our approach may scan all underlying activation mechanisms from the first to last one based on order statistics. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis for the proposed model. Moreover, some discussions on the model selection to compare the fitted models are given. In particular, case deletion influence diagnostics are developed for the joint posterior distribution based on the ψ-divergence, which includes the Kullback-Leibler (K-L), J-distance, L1 norm and χ2-square divergence measures as particular cases. Simulation studies are performed for study frequentist properties of the Bayesian estimates. The methodology is illustrated on a real malignant melanoma data. |
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Repositório Institucional da UNESP |
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Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanismBirnbaum-Saunders distributionCure fraction modelsGeometric distributiono lifetime datasensitivity analysisIn this paper we propose a new cure rate survival model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes which may be responsible for the occurrence of the event of interest is assumed to follow a geometric distribution while the time to event is assumed to follow a Birnbaum-Saunders distribution. As an advantage our approach may scan all underlying activation mechanisms from the first to last one based on order statistics. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis for the proposed model. Moreover, some discussions on the model selection to compare the fitted models are given. In particular, case deletion influence diagnostics are developed for the joint posterior distribution based on the ψ-divergence, which includes the Kullback-Leibler (K-L), J-distance, L1 norm and χ2-square divergence measures as particular cases. Simulation studies are performed for study frequentist properties of the Bayesian estimates. The methodology is illustrated on a real malignant melanoma data.Instituto de Ciências Matemáticas e de Computação Universidade de São PauloUnivesidade Estadual Paulista 'Julio de Mesquita Filho' FEBDepartment of Statistics Storrs University of ConnecticutUnivesidade Estadual Paulista 'Julio de Mesquita Filho' FEBUniversidade de São Paulo (USP)Universidade Estadual Paulista (UNESP)University of ConnecticutBarriga, Gladys D.C. [UNESP]Dey, Dipak K.Cancho, Vicente G.Suzuki, Adriano K.2022-04-28T19:28:34Z2022-04-28T19:28:34Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article35-51http://dx.doi.org/10.3233/MAS-190477Model Assisted Statistics and Applications, v. 15, n. 1, p. 35-51, 2020.1574-1699http://hdl.handle.net/11449/22145610.3233/MAS-1904772-s2.0-85083069439Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengModel Assisted Statistics and Applicationsinfo:eu-repo/semantics/openAccess2022-04-28T19:28:34Zoai:repositorio.unesp.br:11449/221456Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:50:49.939052Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism |
title |
Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism |
spellingShingle |
Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism Barriga, Gladys D.C. [UNESP] Birnbaum-Saunders distribution Cure fraction models Geometric distribution o lifetime data sensitivity analysis |
title_short |
Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism |
title_full |
Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism |
title_fullStr |
Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism |
title_full_unstemmed |
Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism |
title_sort |
Bayesian analysis of Birnbaum-Saunders survival model with cure fraction under a variety of activation mechanism |
author |
Barriga, Gladys D.C. [UNESP] |
author_facet |
Barriga, Gladys D.C. [UNESP] Dey, Dipak K. Cancho, Vicente G. Suzuki, Adriano K. |
author_role |
author |
author2 |
Dey, Dipak K. Cancho, Vicente G. Suzuki, Adriano K. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (UNESP) University of Connecticut |
dc.contributor.author.fl_str_mv |
Barriga, Gladys D.C. [UNESP] Dey, Dipak K. Cancho, Vicente G. Suzuki, Adriano K. |
dc.subject.por.fl_str_mv |
Birnbaum-Saunders distribution Cure fraction models Geometric distribution o lifetime data sensitivity analysis |
topic |
Birnbaum-Saunders distribution Cure fraction models Geometric distribution o lifetime data sensitivity analysis |
description |
In this paper we propose a new cure rate survival model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes which may be responsible for the occurrence of the event of interest is assumed to follow a geometric distribution while the time to event is assumed to follow a Birnbaum-Saunders distribution. As an advantage our approach may scan all underlying activation mechanisms from the first to last one based on order statistics. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis for the proposed model. Moreover, some discussions on the model selection to compare the fitted models are given. In particular, case deletion influence diagnostics are developed for the joint posterior distribution based on the ψ-divergence, which includes the Kullback-Leibler (K-L), J-distance, L1 norm and χ2-square divergence measures as particular cases. Simulation studies are performed for study frequentist properties of the Bayesian estimates. The methodology is illustrated on a real malignant melanoma data. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 2022-04-28T19:28:34Z 2022-04-28T19:28:34Z |
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 |
http://dx.doi.org/10.3233/MAS-190477 Model Assisted Statistics and Applications, v. 15, n. 1, p. 35-51, 2020. 1574-1699 http://hdl.handle.net/11449/221456 10.3233/MAS-190477 2-s2.0-85083069439 |
url |
http://dx.doi.org/10.3233/MAS-190477 http://hdl.handle.net/11449/221456 |
identifier_str_mv |
Model Assisted Statistics and Applications, v. 15, n. 1, p. 35-51, 2020. 1574-1699 10.3233/MAS-190477 2-s2.0-85083069439 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Model Assisted Statistics and Applications |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
35-51 |
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_ |
1808128283863154688 |