Inferences on stress-strength reliability based on ranked set sampling data in case of Lindley distribution

In this study, we consider point and interval estimation of stress-strength reliability R = P(X < Y) based on ranked set sampling when the distribution of the stress and the strength are both Lindley. Firstly, maximum likelihood (ML) estimator of R is obtained. Then, we find asymptotic distribution of ML estimator of R to construct the asymptotic confidence interval. Furthermore, bootstrap confidence intervals of R are constructed using two different resampling methods. The performances of proposed methods are compared with their simple random sampling counterparts via an extensive Monte-Carlo simulation study. At the end of the study, a real data set is analysed for illustrative purposes.