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


Akgul F. G., Acitas S., ŞENOĞLU B.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, vol.88, no.15, pp.3018-3032, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 88 Issue: 15
  • Publication Date: 2018
  • Doi Number: 10.1080/00949655.2018.1498095
  • Journal Name: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3018-3032
  • Keywords: Stress-strength reliability, ranked set sampling, Lindley distribution, estimation, Monte-Carlo simulation, LESS-THAN X), EXPONENTIAL-DISTRIBUTION, WEIBULL DISTRIBUTION, UNBIASED ESTIMATION, CENSORED SAMPLES, PARAMETER, Y)
  • Karadeniz Technical University Affiliated: No

Abstract

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.