Assessing the Performance of Some Ranked Set Sampling Designs Using HybridApproach

In this paper, a joint analysis consisting of goodness-of-fit tests and Markov chain Monte Carlo simulations are used to assess the performance of some ranked set sampling designs. The Markov chain Monte Carlo sim-ulations are conducted when Bayesian methods with Jeffery's priors of the unknown parameters of Weibull distribution are used, while the goodness of fit analysis is conducted when the likelihood estimators are used and the corresponding empirical distributions are obtained. The ranked set sampling designs considered in this research are the usual ranked set sampling, extreme ranked set sampling, median ranked set sampling, and neoteric ranked set sampling designs. An intensive Monte Carlo simulation study is conducted using Lindley's approximation algorithm to compute the different designs' -based estimators. The study showed that the dependent design "neoteric ranked set sampling design" is superior to other ranked set designs and the total relative efficiency is higher than the other designs' total relative efficiency.