A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.57805/revstat.v21i4.425 |
Resumo: | The Burr XII distribution is a flexible model for failure-time data. A very general and commonly observed structure for failure-time data involves left truncation and right censoring. In this article, modelling of left truncated right censored failure-time data by the Burr XII distribution is discussed. The steps of the stochastic expectation maximization algorithm, which is a useful technique of estimation for incomplete data structures, are developed to estimate the model parameters of the Burr XII distribution. The Newton-Raphson method, which is a direct method of obtaining maximum likelihood estimates by optimizing the observed likelihood is also used. The two methods of inference are assessed and compared through a Monte Carlo simulation study. Discussions of the inferential methods are extended to the cases of a three-parameter Burr XII model, and a covariate-included model. An illustrative example based on real data is provided. Finally, an application of the inferential results to a prediction issue is discussed with an illustration. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII DistributionLifetime distributionCensoringTruncationStochastic expectation maximization algorithmPredictionThe Burr XII distribution is a flexible model for failure-time data. A very general and commonly observed structure for failure-time data involves left truncation and right censoring. In this article, modelling of left truncated right censored failure-time data by the Burr XII distribution is discussed. The steps of the stochastic expectation maximization algorithm, which is a useful technique of estimation for incomplete data structures, are developed to estimate the model parameters of the Burr XII distribution. The Newton-Raphson method, which is a direct method of obtaining maximum likelihood estimates by optimizing the observed likelihood is also used. The two methods of inference are assessed and compared through a Monte Carlo simulation study. Discussions of the inferential methods are extended to the cases of a three-parameter Burr XII model, and a covariate-included model. An illustrative example based on real data is provided. Finally, an application of the inferential results to a prediction issue is discussed with an illustration.Statistics Portugal2023-11-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.57805/revstat.v21i4.425https://doi.org/10.57805/revstat.v21i4.425REVSTAT-Statistical Journal; Vol. 21 No. 4 (2023): REVSTAT-Statistical Journal; 447–468REVSTAT; Vol. 21 N.º 4 (2023): REVSTAT-Statistical Journal; 447–4682183-03711645-6726reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://revstat.ine.pt/index.php/REVSTAT/article/view/425https://revstat.ine.pt/index.php/REVSTAT/article/view/425/662Mitra , Debanjaninfo:eu-repo/semantics/openAccess2023-11-11T06:30:21Zoai:revstat:article/425Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:37:57.396639Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution |
title |
A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution |
spellingShingle |
A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution Mitra , Debanjan Lifetime distribution Censoring Truncation Stochastic expectation maximization algorithm Prediction |
title_short |
A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution |
title_full |
A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution |
title_fullStr |
A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution |
title_full_unstemmed |
A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution |
title_sort |
A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution |
author |
Mitra , Debanjan |
author_facet |
Mitra , Debanjan |
author_role |
author |
dc.contributor.author.fl_str_mv |
Mitra , Debanjan |
dc.subject.por.fl_str_mv |
Lifetime distribution Censoring Truncation Stochastic expectation maximization algorithm Prediction |
topic |
Lifetime distribution Censoring Truncation Stochastic expectation maximization algorithm Prediction |
description |
The Burr XII distribution is a flexible model for failure-time data. A very general and commonly observed structure for failure-time data involves left truncation and right censoring. In this article, modelling of left truncated right censored failure-time data by the Burr XII distribution is discussed. The steps of the stochastic expectation maximization algorithm, which is a useful technique of estimation for incomplete data structures, are developed to estimate the model parameters of the Burr XII distribution. The Newton-Raphson method, which is a direct method of obtaining maximum likelihood estimates by optimizing the observed likelihood is also used. The two methods of inference are assessed and compared through a Monte Carlo simulation study. Discussions of the inferential methods are extended to the cases of a three-parameter Burr XII model, and a covariate-included model. An illustrative example based on real data is provided. Finally, an application of the inferential results to a prediction issue is discussed with an illustration. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-11-09 |
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 |
https://doi.org/10.57805/revstat.v21i4.425 https://doi.org/10.57805/revstat.v21i4.425 |
url |
https://doi.org/10.57805/revstat.v21i4.425 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revstat.ine.pt/index.php/REVSTAT/article/view/425 https://revstat.ine.pt/index.php/REVSTAT/article/view/425/662 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Statistics Portugal |
publisher.none.fl_str_mv |
Statistics Portugal |
dc.source.none.fl_str_mv |
REVSTAT-Statistical Journal; Vol. 21 No. 4 (2023): REVSTAT-Statistical Journal; 447–468 REVSTAT; Vol. 21 N.º 4 (2023): REVSTAT-Statistical Journal; 447–468 2183-0371 1645-6726 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134938051641344 |