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Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data

Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in esti...

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Detalles Bibliográficos
Autores principales: Guure, Chris Bambey, Ibrahim, Noor Akma, Adam, Mohd Bakri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556417/
https://www.ncbi.nlm.nih.gov/pubmed/23476718
http://dx.doi.org/10.1155/2013/849520
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author Guure, Chris Bambey
Ibrahim, Noor Akma
Adam, Mohd Bakri
author_facet Guure, Chris Bambey
Ibrahim, Noor Akma
Adam, Mohd Bakri
author_sort Guure, Chris Bambey
collection PubMed
description Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. This study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian. A simulation study is carried out to compare the performances of the methods. A real data application is also illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters.
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spelling pubmed-35564172013-03-09 Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data Guure, Chris Bambey Ibrahim, Noor Akma Adam, Mohd Bakri Comput Math Methods Med Research Article Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. This study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian. A simulation study is carried out to compare the performances of the methods. A real data application is also illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters. Hindawi Publishing Corporation 2013 2013-01-10 /pmc/articles/PMC3556417/ /pubmed/23476718 http://dx.doi.org/10.1155/2013/849520 Text en Copyright © 2013 Chris Bambey Guure et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Guure, Chris Bambey
Ibrahim, Noor Akma
Adam, Mohd Bakri
Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data
title Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data
title_full Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data
title_fullStr Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data
title_full_unstemmed Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data
title_short Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data
title_sort bayesian inference of the weibull model based on interval-censored survival data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556417/
https://www.ncbi.nlm.nih.gov/pubmed/23476718
http://dx.doi.org/10.1155/2013/849520
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