Mean Estimation Using Even Order Ranked Set Sampling

Abstract

An efficient estimate of the population mean based on ranked set sample is of major concern with the cost and success in ranking. In this research an efficient mean estimator based on even order ranked set sampling (EORSS) is proposed and analyzed. The EORSS scheme presents an unbiased estimator when the distribution is symmetric. The performance of population mean estimator based on EORSS is compared with its counterparts in simple random sampling (SRS), ranked set sampling (RSS) as well as extreme ranked set sampling (ERSS) using theoretical and simulation studies. The simulation results validate the theoretical results and show that EORSS mean estimator is always more efficient than SRS mean estimator, equal or more efficient than RSS mean estimator and more efficient than ERSS mean estimator for symmetric and non-symmetric distributions considered in this study. An explicatory application to real-life data set is also presented to demonstrate the achievement of the suggested EORSS mean estimator.