Generalized Exponential Ratio Type Estimator for the Finite Population Mean Under Ranked Set Sampling

Abstract

In this study, we introduce a novel approach for estimating the mean of a finite population using Ranked Set Sampling (RSS), termed the generalized exponential ratio estimator. We derive expressions for the bias and mean squared error (MSE) of the proposed estimator up to the first order of approximation. To assess its performance, we conduct a thorough theoretical and numerical analysis using simulated and real data. Our results demonstrate that the generalized exponential ratio estimator outperforms both the classical ratio estimator and the estimator proposed by Kadilar et al. (2009) under RSS, highlighting its superior efficiency.