Yadav, Abhimanyu S.Vishwakarma, P.K.Bakouch, H.S.Kumar, UpendraChauhan, S.2025-01-272025-01-27202219322321https://dl.bhu.ac.in/ir/handle/123456789/12589In 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.Interval estimation of RCMCMC methodPoint estimationArticlehttps://doi.org/10.24412/1932-2321-2022-471-392-412