Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | |
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.v20i5.389 |
Resumo: | In this article, we consider the estimation of unknown parameters of Weibull distribution when the lifetime data are observed in the presence of progressively type–I hybrid censoring scheme. The Newton–Raphson algorithm, Expectation–Maximization (EM) algorithm and Stochastic EM algorithm are utilized to derive the maximum likelihood estimates for the unknown parameters. Moreover, Bayesian estimators using Tierney–Kadane Method and Markov Chain Monte Carlo method are obtained under three different loss functions, namely, squared error loss, linear–exponential and generalized entropy loss functions. Also, the shrinkage pre–test estimators are derived. An extensive Monte Carlo simulation experiment is conducted under different schemes so that the performances of the listed estimators are compared using mean squared error, confidence interval length and coverage probabilities. Asymptotic normality and MCMC samples are used to obtain the confidence intervals and highest posterior density intervals respectively. Further, a real data example is presented to illustrate the methods. Finally, some conclusive remarks are presented. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored DataBayesian estimationEM algorithmSEM algorithmTierney-Kadane’s approximationprogressively type-I hybrid censoringWeibull distributionIn this article, we consider the estimation of unknown parameters of Weibull distribution when the lifetime data are observed in the presence of progressively type–I hybrid censoring scheme. The Newton–Raphson algorithm, Expectation–Maximization (EM) algorithm and Stochastic EM algorithm are utilized to derive the maximum likelihood estimates for the unknown parameters. Moreover, Bayesian estimators using Tierney–Kadane Method and Markov Chain Monte Carlo method are obtained under three different loss functions, namely, squared error loss, linear–exponential and generalized entropy loss functions. Also, the shrinkage pre–test estimators are derived. An extensive Monte Carlo simulation experiment is conducted under different schemes so that the performances of the listed estimators are compared using mean squared error, confidence interval length and coverage probabilities. Asymptotic normality and MCMC samples are used to obtain the confidence intervals and highest posterior density intervals respectively. Further, a real data example is presented to illustrate the methods. Finally, some conclusive remarks are presented.Statistics Portugal2023-02-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.57805/revstat.v20i5.389https://doi.org/10.57805/revstat.v20i5.389REVSTAT-Statistical Journal; Vol. 20 No. 5 (2022): REVSTAT-Statistical Journal; 563-586REVSTAT; Vol. 20 N.º 5 (2022): REVSTAT-Statistical Journal; 563-5862183-03711645-6726reponame: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/389https://revstat.ine.pt/index.php/REVSTAT/article/view/389/601Copyright (c) 2021 REVSTAT-Statistical Journalinfo:eu-repo/semantics/openAccessAsar , YasinArabi Belaghi , Reza2023-03-04T06:30:14Zoai:revstat:article/389Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:48:11.141668Repositório 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 |
Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data |
title |
Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data |
spellingShingle |
Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data Asar , Yasin Bayesian estimation EM algorithm SEM algorithm Tierney-Kadane’s approximation progressively type-I hybrid censoring Weibull distribution |
title_short |
Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data |
title_full |
Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data |
title_fullStr |
Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data |
title_full_unstemmed |
Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data |
title_sort |
Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data |
author |
Asar , Yasin |
author_facet |
Asar , Yasin Arabi Belaghi , Reza |
author_role |
author |
author2 |
Arabi Belaghi , Reza |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Asar , Yasin Arabi Belaghi , Reza |
dc.subject.por.fl_str_mv |
Bayesian estimation EM algorithm SEM algorithm Tierney-Kadane’s approximation progressively type-I hybrid censoring Weibull distribution |
topic |
Bayesian estimation EM algorithm SEM algorithm Tierney-Kadane’s approximation progressively type-I hybrid censoring Weibull distribution |
description |
In this article, we consider the estimation of unknown parameters of Weibull distribution when the lifetime data are observed in the presence of progressively type–I hybrid censoring scheme. The Newton–Raphson algorithm, Expectation–Maximization (EM) algorithm and Stochastic EM algorithm are utilized to derive the maximum likelihood estimates for the unknown parameters. Moreover, Bayesian estimators using Tierney–Kadane Method and Markov Chain Monte Carlo method are obtained under three different loss functions, namely, squared error loss, linear–exponential and generalized entropy loss functions. Also, the shrinkage pre–test estimators are derived. An extensive Monte Carlo simulation experiment is conducted under different schemes so that the performances of the listed estimators are compared using mean squared error, confidence interval length and coverage probabilities. Asymptotic normality and MCMC samples are used to obtain the confidence intervals and highest posterior density intervals respectively. Further, a real data example is presented to illustrate the methods. Finally, some conclusive remarks are presented. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-27 |
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.v20i5.389 https://doi.org/10.57805/revstat.v20i5.389 |
url |
https://doi.org/10.57805/revstat.v20i5.389 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revstat.ine.pt/index.php/REVSTAT/article/view/389 https://revstat.ine.pt/index.php/REVSTAT/article/view/389/601 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 REVSTAT-Statistical Journal info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 REVSTAT-Statistical Journal |
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. 20 No. 5 (2022): REVSTAT-Statistical Journal; 563-586 REVSTAT; Vol. 20 N.º 5 (2022): REVSTAT-Statistical Journal; 563-586 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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1799130952913387520 |