Bayesian parameter and reliability estimate of Weibull failure time distribution

Bayes and frequentist estimators are obtained for the two-parameter Weibull failure time distribution with uncensored observations as well as the survival/reliability and hazard function. The Weibull distribution is used extensively in life testing and reliability/survival analysis. The Bayes approa...

Full description

Bibliographic Details
Main Author: Nur Munawwarah
Other Authors: Chris Bambey Guure
Format: Journal
Published: Bulletin of the Malaysian Mathematical Sciences Society , Malaysian Mathematical Sciences Society, Universiti Sains Malaysia, Springer 2014
Subjects:
Online Access:http://www.myjurnal.my/public/article-view.php?id=84140
id oai:www.myjurnal.my:84140
recordtype eprints
spelling oai:www.myjurnal.my:841402018-09-20T00:00:00Z Bayesian parameter and reliability estimate of Weibull failure time distribution Nur Munawwarah Mathematics & statistics Bayes and frequentist estimators are obtained for the two-parameter Weibull failure time distribution with uncensored observations as well as the survival/reliability and hazard function. The Weibull distribution is used extensively in life testing and reliability/survival analysis. The Bayes approach is obtained using Lindleys approximation technique with standard non-informative (vague) prior and a proposed generalisation of the noninformative prior. A simulation study is carried out to compare the performances of the methods. It is observed from the study that the unknown parameters, the reliability and hazard functions are best estimated by Bayes using linear exponential loss with the proposed prior followed by general entropy loss function. Bulletin of the Malaysian Mathematical Sciences Society , Malaysian Mathematical Sciences Society, Universiti Sains Malaysia, Springer Chris Bambey Guure 2014-00-00 Journal application/pdf 84140 www.myjurnal.my/filebank/published_article/3479101.pdf www.myjurnal.my/public/article-view.php?id=84140
repository_type Digital Repository
institution_category Local Institution
institution MyJournal
building MyJournal Repository
collection Online Access
topic Mathematics & statistics
spellingShingle Mathematics & statistics
Nur Munawwarah
Bayesian parameter and reliability estimate of Weibull failure time distribution
description Bayes and frequentist estimators are obtained for the two-parameter Weibull failure time distribution with uncensored observations as well as the survival/reliability and hazard function. The Weibull distribution is used extensively in life testing and reliability/survival analysis. The Bayes approach is obtained using Lindleys approximation technique with standard non-informative (vague) prior and a proposed generalisation of the noninformative prior. A simulation study is carried out to compare the performances of the methods. It is observed from the study that the unknown parameters, the reliability and hazard functions are best estimated by Bayes using linear exponential loss with the proposed prior followed by general entropy loss function.
author2 Chris Bambey Guure
author_facet Chris Bambey Guure
Nur Munawwarah
format Journal
author Nur Munawwarah
author_sort Nur Munawwarah
title Bayesian parameter and reliability estimate of Weibull failure time distribution
title_short Bayesian parameter and reliability estimate of Weibull failure time distribution
title_full Bayesian parameter and reliability estimate of Weibull failure time distribution
title_fullStr Bayesian parameter and reliability estimate of Weibull failure time distribution
title_full_unstemmed Bayesian parameter and reliability estimate of Weibull failure time distribution
title_sort bayesian parameter and reliability estimate of weibull failure time distribution
publisher Bulletin of the Malaysian Mathematical Sciences Society , Malaysian Mathematical Sciences Society, Universiti Sains Malaysia, Springer
publishDate 2014
url http://www.myjurnal.my/public/article-view.php?id=84140
first_indexed 2018-09-20T12:02:59Z
last_indexed 2018-09-20T12:02:59Z
_version_ 1612235378614337536