Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations
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.v21i3.174 |
Resumo: | In this article, the classical as well as the Bayesian estimation problems of two-parameter Burr type XII distribution based on fuzzy data are considered. The maximum likelihood estimators via two methods, namely, Newton-Raphson and Expectation-Maximization algorithms are computed. The standard errors of the estimates are computed using the observed information matrix. For computing the Bayes estimators, three methods viz Lindley’s approximation, Tierney-Kadane approximation and highest posterior density method are obtained. Monte-Carlo simulation experiments are conducted to investigate the performance of the proposed methods. Finally, the proposed methods are illustrated by using three different real data sets. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Estimating the Parameters of Burr Type XII Distribution with Fuzzy ObservationsBayesian estimationBurr type XII distributionexpectation-maximization algorithmfuzzy observationsLindley’s approximationmaximum likelihood estimationTierney-Kadane approximationIn this article, the classical as well as the Bayesian estimation problems of two-parameter Burr type XII distribution based on fuzzy data are considered. The maximum likelihood estimators via two methods, namely, Newton-Raphson and Expectation-Maximization algorithms are computed. The standard errors of the estimates are computed using the observed information matrix. For computing the Bayes estimators, three methods viz Lindley’s approximation, Tierney-Kadane approximation and highest posterior density method are obtained. Monte-Carlo simulation experiments are conducted to investigate the performance of the proposed methods. Finally, the proposed methods are illustrated by using three different real data sets.Statistics Portugal2023-07-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.57805/revstat.v21i3.174https://doi.org/10.57805/revstat.v21i3.174REVSTAT-Statistical Journal; Vol. 21 No. 3 (2023): REVSTAT-Statistical Journal; 405–424REVSTAT; Vol. 21 N.º 3 (2023): REVSTAT-Statistical Journal; 405–4242183-03711645-6726reponame: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/174https://revstat.ine.pt/index.php/REVSTAT/article/view/174/653Copyright (c) 2022 REVSTAT-Statistical Journalinfo:eu-repo/semantics/openAccessAbdul Hussein , AbbasAl-Mosawi , Riyadh2023-08-12T06:30:21Zoai:revstat:article/174Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T14:59:51.038591Repositório 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 |
Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations |
title |
Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations |
spellingShingle |
Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations Abdul Hussein , Abbas Bayesian estimation Burr type XII distribution expectation-maximization algorithm fuzzy observations Lindley’s approximation maximum likelihood estimation Tierney-Kadane approximation |
title_short |
Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations |
title_full |
Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations |
title_fullStr |
Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations |
title_full_unstemmed |
Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations |
title_sort |
Estimating the Parameters of Burr Type XII Distribution with Fuzzy Observations |
author |
Abdul Hussein , Abbas |
author_facet |
Abdul Hussein , Abbas Al-Mosawi , Riyadh |
author_role |
author |
author2 |
Al-Mosawi , Riyadh |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Abdul Hussein , Abbas Al-Mosawi , Riyadh |
dc.subject.por.fl_str_mv |
Bayesian estimation Burr type XII distribution expectation-maximization algorithm fuzzy observations Lindley’s approximation maximum likelihood estimation Tierney-Kadane approximation |
topic |
Bayesian estimation Burr type XII distribution expectation-maximization algorithm fuzzy observations Lindley’s approximation maximum likelihood estimation Tierney-Kadane approximation |
description |
In this article, the classical as well as the Bayesian estimation problems of two-parameter Burr type XII distribution based on fuzzy data are considered. The maximum likelihood estimators via two methods, namely, Newton-Raphson and Expectation-Maximization algorithms are computed. The standard errors of the estimates are computed using the observed information matrix. For computing the Bayes estimators, three methods viz Lindley’s approximation, Tierney-Kadane approximation and highest posterior density method are obtained. Monte-Carlo simulation experiments are conducted to investigate the performance of the proposed methods. Finally, the proposed methods are illustrated by using three different real data sets. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-31 |
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.v21i3.174 https://doi.org/10.57805/revstat.v21i3.174 |
url |
https://doi.org/10.57805/revstat.v21i3.174 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revstat.ine.pt/index.php/REVSTAT/article/view/174 https://revstat.ine.pt/index.php/REVSTAT/article/view/174/653 |
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; Vol. 21 No. 3 (2023): REVSTAT-Statistical Journal; 405–424 REVSTAT; Vol. 21 N.º 3 (2023): REVSTAT-Statistical Journal; 405–424 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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1799129870937096192 |