A Novel Sub-Type Mean Estimator for Ranked Set Sampling with Dual Auxiliary Variables

Abstract

This research introduces a novel sub-estimator designed to estimate the population mean under ranked set sampling, motivated by the new concept of a recently introduced sub-ratio estimator. The mathematical formulas of the proposed estimator’s mean square error and bias are presented and theoretically contrasted with an analogous estimator found in the existing best sub-estimator literature. In addition to the theoretical analysis, empirical evidence is provided to validate the superiority of the proposed estimator. This empirical validation is based on numerical computations using Monte Carlo simulations, encompassing synthetic and real data applications. The results underscore the effectiveness of the proposed estimator. Finally, this study discusses the need for further research.