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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.
Muhammad Noor-ul-Amin, Muhammad Tayyab, Muhammad Hanif. (2019) Mean Estimation Using Even Order Ranked Set Sampling, Punjab University Journal of Mathematics, Volume 51, Issue 1.
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