AbstractAbstractRanked set sampling (RSS) is a cost-efficient alternative to simple random (SRS) when ranking small sets of population units much simpler than effectively measuring them. Maximum likelihood estimation (MLE) in RSS usually based on the restrictive assumption perfect ranking, assuming that sample lead order statistics. However, this procedure not flexible and often produces highly...

In this study, we look at how to estimate stress–strength reliability models, R1 = P (Y < X) and R2 X), where the strength X stress Y have same distribution in first model, R1, Z different distributions second R2. Based on are assumed Lomax distributions, whereas, both inverse respectively. With assumption that variables models independent, median-ranked set sampling (MRSS) strategy is used ...

This paper presents improved population mean estimators using auxiliary variables in Stratified Ranked Set Sampling. We have derived the expressions for bias and square errors up to first order of approximation shown that proposed under optimum conditions are more efficient than other taken this paper. In an attempt verify efficiencies estimators, theoretical results supported by numerical illu...

This paper considers some efficient combined and separate classes of estimators the population mean in presence bivariate auxiliary information under stratified ranked set sampling. The square error MSE expressions proffered are derived to first order approxima...