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Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime
In this paper, we propose a hierarchical statistical model for a single repairable system subject to several failure modes (competing risks). The paper describes how complex engineered systems may be modelled hierarchically by use of Bayesian methods. It is also assumed that repairs are minimal and...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360570/ https://www.ncbi.nlm.nih.gov/pubmed/34383829 http://dx.doi.org/10.1371/journal.pone.0255944 |
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author | Louzada, Francisco Cuminato, José A. Rodriguez, Oscar M. H. Tomazella, Vera L. D. Ferreira, Paulo H. Ramos, Pedro L. Milani, Eder A. Bochio, Gustavo Perissini, Ivan C. Gonzatto Junior, Oilson A. Mota, Alex L. Alegría, Luis F. A. Colombo, Danilo Perondi, Eduardo A. Wentz, André V. Júnior, Anselmo L. Silva Barone, Dante A. C. Santos, Hugo F. L. Magalhães, Marcus V. C. |
author_facet | Louzada, Francisco Cuminato, José A. Rodriguez, Oscar M. H. Tomazella, Vera L. D. Ferreira, Paulo H. Ramos, Pedro L. Milani, Eder A. Bochio, Gustavo Perissini, Ivan C. Gonzatto Junior, Oilson A. Mota, Alex L. Alegría, Luis F. A. Colombo, Danilo Perondi, Eduardo A. Wentz, André V. Júnior, Anselmo L. Silva Barone, Dante A. C. Santos, Hugo F. L. Magalhães, Marcus V. C. |
author_sort | Louzada, Francisco |
collection | PubMed |
description | In this paper, we propose a hierarchical statistical model for a single repairable system subject to several failure modes (competing risks). The paper describes how complex engineered systems may be modelled hierarchically by use of Bayesian methods. It is also assumed that repairs are minimal and each failure mode has a power-law intensity. Our proposed model generalizes another one already presented in the literature and continues the study initiated by us in another published paper. Some properties of the new model are discussed. We conduct statistical inference under an objective Bayesian framework. A simulation study is carried out to investigate the efficiency of the proposed methods. Finally, our methodology is illustrated by two practical situations currently addressed in a project under development arising from a partnership between Petrobras and six research institutes. |
format | Online Article Text |
id | pubmed-8360570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83605702021-08-13 Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime Louzada, Francisco Cuminato, José A. Rodriguez, Oscar M. H. Tomazella, Vera L. D. Ferreira, Paulo H. Ramos, Pedro L. Milani, Eder A. Bochio, Gustavo Perissini, Ivan C. Gonzatto Junior, Oilson A. Mota, Alex L. Alegría, Luis F. A. Colombo, Danilo Perondi, Eduardo A. Wentz, André V. Júnior, Anselmo L. Silva Barone, Dante A. C. Santos, Hugo F. L. Magalhães, Marcus V. C. PLoS One Research Article In this paper, we propose a hierarchical statistical model for a single repairable system subject to several failure modes (competing risks). The paper describes how complex engineered systems may be modelled hierarchically by use of Bayesian methods. It is also assumed that repairs are minimal and each failure mode has a power-law intensity. Our proposed model generalizes another one already presented in the literature and continues the study initiated by us in another published paper. Some properties of the new model are discussed. We conduct statistical inference under an objective Bayesian framework. A simulation study is carried out to investigate the efficiency of the proposed methods. Finally, our methodology is illustrated by two practical situations currently addressed in a project under development arising from a partnership between Petrobras and six research institutes. Public Library of Science 2021-08-12 /pmc/articles/PMC8360570/ /pubmed/34383829 http://dx.doi.org/10.1371/journal.pone.0255944 Text en © 2021 Louzada et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Louzada, Francisco Cuminato, José A. Rodriguez, Oscar M. H. Tomazella, Vera L. D. Ferreira, Paulo H. Ramos, Pedro L. Milani, Eder A. Bochio, Gustavo Perissini, Ivan C. Gonzatto Junior, Oilson A. Mota, Alex L. Alegría, Luis F. A. Colombo, Danilo Perondi, Eduardo A. Wentz, André V. Júnior, Anselmo L. Silva Barone, Dante A. C. Santos, Hugo F. L. Magalhães, Marcus V. C. Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime |
title | Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime |
title_full | Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime |
title_fullStr | Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime |
title_full_unstemmed | Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime |
title_short | Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime |
title_sort | improved objective bayesian estimator for a plp model hierarchically represented subject to competing risks under minimal repair regime |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360570/ https://www.ncbi.nlm.nih.gov/pubmed/34383829 http://dx.doi.org/10.1371/journal.pone.0255944 |
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