Akgül, Fatma GülŞenoğlu, Birdal2025-07-072025-07-07201718162711https://hdl.handle.net/11494/5680In statistical literature, estimation of R=P(X < Y) is a commonly-investigated problem, and consequently, there have been considerable number of studies dealing with its estimation of it under simple random sampling (SRS). However, in recent years, the ranked set sampling (RSS) method have been widely-used in the estimation of R. In this study, we consider the estimation of R when the distribution of the both stress and strength are Weibull under the modification of RSS, which are extreme ranked set sampling (ERSS), median ranked set sampling (MRSS) and percentile ranked set sampling (PRSS). We obtain the estimators of R using the maximum likelihood (ML) and the modified maximum likelihood (MML) methodologies under these modifications. Then the performances of proposed estimators are compared with the corresponding ML and MML estimators of R using SRS via a Monte-Carlo simulation study.eninfo:eu-repo/semantics/openAccessEfficiencyExtreme ranked set samplingMedian ranked set samplingPercentile ranked set samplingStress-strength modelArticle13493195810.18187/pjsor.v13i4.2056Q2WOS:000423943700015Q3