Improved estimator of finite population mean using auxiliary attribute in stratified random sampling

TL;DR

This paper proposes an improved estimator for the finite population mean in stratified random sampling by leveraging the point bi-serial correlation with an auxiliary attribute, enhancing estimation accuracy.

Contribution

It introduces a new estimator utilizing auxiliary attribute information and derives its bias and mean square error expressions, demonstrating its advantages over existing methods.

Findings

The proposed estimator shows reduced bias and mean square error.

Empirical results confirm its superiority over traditional estimators.

The method effectively utilizes auxiliary attribute information in stratified sampling.

Abstract

The present study discuss the problem of estimating the finite population mean using auxiliary attribute in stratified random sampling. In this paper taking the advantage of point bi-serial correlation between the study variable and auxiliary attribute, we have improved the estimation of population mean in stratified random sampling. The expressions for Bias and Mean square error have been derived under stratified random sampling. In addition, an empirical study has been carried out to examine the merits of the proposed estimator over the existing estimators.