Inference for multicomponent stress-strength reliability based on generalized Lindley distribution

This paper explores the classical and Bayesian estimation of multicomponent stress strength reliability when both the stress and strength variables follow the generalized Lindley distribution. The maximum likelihood (ML) and Bayesian methods are utilized in the estimation procedure. The Bayesian estimates are obtained by using Lindley's approximation, Tierney-Kadane approximation and Markov Chain Monte Carlo (MCMC) methods due to the lack of explicit forms. The asymptotic confidence intervals are constructed based on the ML estimators. The MCMC method is used to construct the Bayesian credible intervals. A Monte Carlo simulation study is conducted to investigate and compare the performance of the proposed methods. Finally, the methodology is applied to a real data set to demonstrate its practical applicability.