Publication:
Confidence intervals for the reliability characteristics via different estimation methods for the power Lindley model

dc.contributor.authorYadav, Abhimanyu S.
dc.contributor.authorVishwakarma, P.K.
dc.contributor.authorBakouch, H.S.
dc.contributor.authorKumar, Upendra
dc.contributor.authorChauhan, S.
dc.date.accessioned2025-01-27T09:40:13Z
dc.date.available2025-01-27T09:40:13Z
dc.date.issued2022
dc.description.abstractIn this article, classical and Bayes interval estimation procedures have been discussed for the reliability characteristics, namely mean time to system failure, reliability function, and hazard function for the power Lindley model and its special case. In the classical part, maximum likelihood estimation, maximum product spacing estimation are discussed to estimate the reliability characteristics. Since the computation of the exact confidence intervals for the reliability characteristics is not directly possible, then, using the large sample theory, the asymptotic confidence interval is constructed using the above-mentioned classical estimation methods. Further, the bootstrap (standard-boot, percentile-boot, students t-boot) confidence intervals are also obtained. Next, Bayes estimators are derived with a gamma prior using squared error loss function and linex loss function. The Bayes credible intervals for the same characteristics are constructed using simulated posterior samples. The obtained estimators are evaluated by the Monte Carlo simulation study in terms of mean square error, average width, and coverage probabilities. A real-life example has also been illustrated for the application purpose. � 2022 Reliability: Theory and Applications. All rights reserved.
dc.identifier.doihttps://doi.org/10.24412/1932-2321-2022-471-392-412
dc.identifier.issn19322321
dc.identifier.urihttps://dl.bhu.ac.in/ir/handle/123456789/12589
dc.publisherGnedenko Forum
dc.subjectInterval estimation of RC
dc.subjectMCMC method
dc.subjectPoint estimation
dc.titleConfidence intervals for the reliability characteristics via different estimation methods for the power Lindley model
dc.typeArticle
dspace.entity.typePublication
journal.titleReliability: Theory and Applications
journalvolume.identifier.volume17

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