A family of median based estimators in simple random sampling

TL;DR

This paper introduces a new family of median-based estimators for simple random sampling that utilize known population parameters, demonstrating their theoretical properties and empirical performance.

Contribution

It proposes a novel family of median-based estimators that encompass existing estimators as special cases, with bias and MSE analysis and empirical validation.

Findings

Proposed estimators have lower bias and MSE than existing methods

Empirical results confirm the superiority of the new estimators

Theoretical analysis supports improved estimation accuracy

Abstract

In this paper we have proposed a median based estimator using known value of some population parameter(s) in simple random sampling. Various existing estimators are shown particular members of the proposed estimator. The bias and mean squared error of the proposed estimator is obtained up to the first order of approximation under simple random sampling without replacement. An empirical study is carried out to judge the superiority of proposed estimator over others.