Use of Auxiliary Information in Variance Estimation

TL;DR

This paper introduces a new class of ratio-type estimators for finite population variance that leverage auxiliary information, deriving their asymptotic mean square error and demonstrating improved performance through empirical analysis.

Contribution

It proposes a novel class of variance estimators using auxiliary data, with derived asymptotic properties and empirical validation showing enhanced accuracy.

Findings

The new estimators have lower mean square error than existing methods.

Asymptotic expressions for MSE are successfully derived.

Empirical results confirm improved estimator performance.

Abstract

This paper proposes a class of ratio type estimators of finite population variance, when the population variance of an auxiliary character is known. Asymptotic expression for mean square error (MSE) is derived and compared with the mean square errors of some existing estimators. An empirical study is carried out to illustrate the performance of the constructed estimator over others.