An improved estimator for population mean using auxiliary information in stratified random sampling

TL;DR

This paper introduces a new estimator for the population mean in stratified random sampling that leverages auxiliary information to improve accuracy, supported by theoretical derivations and empirical validation.

Contribution

A novel estimator for the population mean in stratified sampling that incorporates auxiliary information, with derived MSE expressions and demonstrated efficiency over existing estimators.

Findings

The proposed estimator has a lower mean squared error than traditional methods.

Theoretical conditions for estimator efficiency are verified through numerical examples.

Empirical results show improved accuracy compared to standard estimators.

Abstract

In the present study, we propose a new estimator for population mean of the study variable y in the case of stratified random sampling using the information based on auxiliary variable x. Expression for the mean squared error (MSE) of the proposed estimators is derived up to the first order of approximation. The theoretical conditions have also been verified by a numerical example. An empirical study is carried out to show the efficiency of the suggested estimator over sample mean estimator, usual separate ratio, separate product estimator and other proposed estimators.