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 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 estimat...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2018-10, Vol.88 (15), p.3018-3032
Main Authors: Akgül, Fatma Gül, Acitas, Sükrü, Senoglu, Birdal
Format: Article
Language:English
Subjects:
Computer simulation
Confidence intervals
estimation
Lindley distribution
Maximum likelihood estimators
Monte Carlo simulation
Random sampling
ranked set sampling
Reliability
Resampling
Stress concentration
Stress-strength reliability
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Summary:In this study, we consider point and interval estimation of stress-strength reliability 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.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2018.1498095