A Note on the Stochastic EM Algorithm Based on Left Truncated Right Censored Data From Burr XII Distribution

Detalhes bibliográficos
Autor(a) principal: Mitra , Debanjan
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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spelling 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
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