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In this paper, point and interval estimation of stress-strength reliability based on lower record ranked set sampling (RRSS) scheme under the proportional reversed hazard rate model are considered. Maximum likelihood, uniformly minimum variance unbiased estimator, and Bayesian estimators of R are derived. Also, we compared this point estimators with their counterparts obtained by well-known sampling scheme in record values known as inverse sampling scheme. Various confidence intervals for the parameter R are constructed, and compared based on the simulation study. Moreover, the RRSS scheme is compared with ordinary records in case of interval estimations. We observed that our proposed point and interval estimations perform well in the estimation of R based on RRSS. We also proved that all calculations do not depend on the baseline distribution in the proportional reversed hazard rate model. Finally, a data set has been analyzed for illustrative purposes. [ABSTRACT FROM AUTHOR]

This paper presents likelihood and Bayesian estimation of stress–strength reliability parameter (say R ) based on lower record ranked set sampling scheme from the generalized exponential distribution. Maximum likelihood, uniformly minimum variance unbiased as well as the Bayesian estimators of R are derived and their properties are studied. Constructing and comparing various confidence intervals for the parameter R is also examined. Finally, the rocket-motor experiment data is analysed for illustrating purposes. [ABSTRACT FROM AUTHOR]