A new ranked set sampling design for estimating population mean and variance based on neoteric ranked set sampling

Mohammed Obeidat; Rahaf Mashhour Na’amneh; Ahmad A. Hanandeh; Mahmoud Zuhier Aldrabseh; Tarek M. Omara

doi:10.46481/jnsps.2026.3529

Authors

Mohammed Obeidat Department of Statistics, Yarmouk University, Irbid, Jordan
Rahaf Mashhour Na’amneh Department of Statistics, Yarmouk University, Irbid, Jordan
Ahmad A. Hanandeh [email protected] Department of Mathematics, Faculty of Science, Islamic University of Madinah, Madinah 42351, Saudi Arabia
Mahmoud Zuhier Aldrabseh Department of Mathematics, Faculty of Science, Zarqa University, Zarqa 13110, Jordan
Tarek M. Omara Department of Economics, Faculty of Business, Islamic University of Madinah, Madinah, Saudi Arabia; Department of Statistics, Mathematics, and Insurance, Faculty of Commerce, Kafr El-Sheikh University, Kafr El-Sheikh, Egypt

Keywords:

Ranked set sampling, Neoteric ranked set sampling, Modified neoteric ranked set sampling, Population mean, Population variance

Abstract

This paper proposes a new ranked set sampling (RSS) design, termed modified neoteric RSS (MNRSS), for efficient estimation of the population mean and variance. The proposed method extends the neoteric RSS (NRSS) framework by selecting sampled units in a more dispersed manner while avoiding extreme ranks, thereby improving population representation and estimation accuracy, particularly for the population variance. The theoretical properties of the proposed mean estimator are examined. Under perfect ranking, the estimator is shown to be unbiased when the underlying distribution is symmetric; for asymmetric distributions, it exhibits only a small bias. The performance of MNRSS was evaluated through an extensive simulation study involving several symmetric and asymmetric distributions. The proposed design was compared with simple random sampling (SRS), RSS, median RSS (MRSS), extreme RSS (ERSS), NRSS, systematic RSS (SRSS), and centralized RSS (CRSS). Performance was assessed using bias and mean square error (MSE). The results show that MNRSS generally outperforms the competing methods, particularly for estimating population variance and under asymmetric distributions. An application to Scots pine tree measurements further supports the practical usefulness of the proposed sampling design.

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