Reliability estimation in multicomponent stress-strength model using weighted exponential-Lindley distribution

Abstract

This research investigates the reliability estimation in multicomponent stress-strength model when both the stress and strengths are drawn from Weighted Exponential-Lindley distribution. Reliability assessment is carried out using classical and Bayesian approaches. The Bayes estimates of the reliability in the multicomponent stress-strength model are derived under a squared error loss function using informative and non-informative priors for the parameters. Further, Lindley's approximation and Gibbs sampling method are used to develop Bayes estimators for the system's reliability due to the lack of explicit forms. Additionally, an asymptotic confidence interval and the highest probability density credible interval are constructed to gauge system performance. A simulation study is conducted to assess the performance of reliability estimators. Finally, a real data set is analysed for illustrative purposes.

KEYWORDS:

• Stress-strength model
• reliability
• Bayesian estimation
• weighted exponential-Lindley distribution
• asymptotic confidence interval
• Gibbs sampling

Disclosure statement

No potential conflict of interest was reported by the author(s).