New ranked set sampling for estimating the population parameters

TL;DR

This paper introduces a unified ranked set sampling (URSS) method that improves the estimation of population mean and variance, outperforming traditional RSS and SRS methods especially with perfect or imperfect ranking.

Contribution

The paper proposes a novel modification of ranked set sampling called URSS, enhancing estimation accuracy for population parameters over existing methods.

Findings

URSS estimators outperform RSS and SRS with perfect ranking.

URSS remains superior even with imperfect ranking.

Simulation and real data demonstrate the efficiency of URSS.

Abstract

In this paper, a new modification of ranked set sampling (RSS) is suggested, namely; unified ranked set sampling (URSS) for estimating the population mean and variance. The performance of the empirical mean and variance estimators based on URSS are compared with their counterparts in ranked set sampling and simple random sampling (SRS) via Monte Carlo simulation. Simulation results indicate that the URSS estimators perform better than their counterparts using RSS and SRS designs when the ranking is perfect. When the ranking is imperfect, the URSS estimators still are superior than their counterparts in ranked set sampling and simple random sampling methods. Finally, an illustrative example is provided to show the efficiency of the new method in practice.