Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://revstat.ine.pt/index.php/REVSTAT/article/view/539 |
Resumo: | The inverse Burr distribution is a significant and commonly used lifetime distribution, which plays an important role in reliability engineering. In this article, the estimation of parameters of the inverse Burr distribution based on generalized Type II progressive hybrid censored sample is studied. The expectation-maximization (EM) algorithm is employed for computing the maximum likelihood estimates of the unknown parameters. It is shown that the maximum likelihood estimates exist uniquely. The asymptotic confidence intervals for the parameters are constructed using the missing value principle. Under Bayesian framework, the Bayes estimators are developed based on Lindley's technique and Metropolis-Hastings algorithm. Furthermore, the highest posterior density (HPD) credible intervals are successively constructed. Finally, simulation experiments are implemented to access performance of several proposed methods in this article, and sewer invert trap real data is presented to exemplify the theoretical outcomes. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022Bayes estimatorsEM algorithmGeneralized Type II progressive hybrid censoringHPD credible intervalInverse Burr distributionSeparation of sewer solidsThe inverse Burr distribution is a significant and commonly used lifetime distribution, which plays an important role in reliability engineering. In this article, the estimation of parameters of the inverse Burr distribution based on generalized Type II progressive hybrid censored sample is studied. The expectation-maximization (EM) algorithm is employed for computing the maximum likelihood estimates of the unknown parameters. It is shown that the maximum likelihood estimates exist uniquely. The asymptotic confidence intervals for the parameters are constructed using the missing value principle. Under Bayesian framework, the Bayes estimators are developed based on Lindley's technique and Metropolis-Hastings algorithm. Furthermore, the highest posterior density (HPD) credible intervals are successively constructed. Finally, simulation experiments are implemented to access performance of several proposed methods in this article, and sewer invert trap real data is presented to exemplify the theoretical outcomes.Statistics Portugal2022-07-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://revstat.ine.pt/index.php/REVSTAT/article/view/539REVSTAT-Statistical Journal; new articleREVSTAT; new article2183-03711645-6726reponame: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/539https://revstat.ine.pt/index.php/REVSTAT/article/view/539/597Copyright (c) 2022 REVSTAT-Statistical Journalinfo:eu-repo/semantics/openAccessAsadi, SaeidPanahi , HaniehParviz, Parya2023-02-11T06:30:12Zoai:revstat:article/539Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:46:25.924439Repositório 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 for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022 |
title |
Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022 |
spellingShingle |
Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022 Asadi, Saeid Bayes estimators EM algorithm Generalized Type II progressive hybrid censoring HPD credible interval Inverse Burr distribution Separation of sewer solids |
title_short |
Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022 |
title_full |
Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022 |
title_fullStr |
Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022 |
title_full_unstemmed |
Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022 |
title_sort |
Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data: Accepted: July 2022 |
author |
Asadi, Saeid |
author_facet |
Asadi, Saeid Panahi , Hanieh Parviz, Parya |
author_role |
author |
author2 |
Panahi , Hanieh Parviz, Parya |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Asadi, Saeid Panahi , Hanieh Parviz, Parya |
dc.subject.por.fl_str_mv |
Bayes estimators EM algorithm Generalized Type II progressive hybrid censoring HPD credible interval Inverse Burr distribution Separation of sewer solids |
topic |
Bayes estimators EM algorithm Generalized Type II progressive hybrid censoring HPD credible interval Inverse Burr distribution Separation of sewer solids |
description |
The inverse Burr distribution is a significant and commonly used lifetime distribution, which plays an important role in reliability engineering. In this article, the estimation of parameters of the inverse Burr distribution based on generalized Type II progressive hybrid censored sample is studied. The expectation-maximization (EM) algorithm is employed for computing the maximum likelihood estimates of the unknown parameters. It is shown that the maximum likelihood estimates exist uniquely. The asymptotic confidence intervals for the parameters are constructed using the missing value principle. Under Bayesian framework, the Bayes estimators are developed based on Lindley's technique and Metropolis-Hastings algorithm. Furthermore, the highest posterior density (HPD) credible intervals are successively constructed. Finally, simulation experiments are implemented to access performance of several proposed methods in this article, and sewer invert trap real data is presented to exemplify the theoretical outcomes. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07-27 |
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://revstat.ine.pt/index.php/REVSTAT/article/view/539 |
url |
https://revstat.ine.pt/index.php/REVSTAT/article/view/539 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revstat.ine.pt/index.php/REVSTAT/article/view/539 https://revstat.ine.pt/index.php/REVSTAT/article/view/539/597 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 REVSTAT-Statistical Journal info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 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; new article REVSTAT; new article 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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1799130935502831616 |