An enhanced estimator of finite population variance using two auxiliary variables under simple random sampling

Abstract

In this article, we have suggested a new improved estimator for estimation of finite population variance under simple random sampling. We use two auxiliary variables to improve the efficiency of estimator. The numerical expressions for the bias and mean square error are derived up to the first order approximation. To evaluate the efficiency of the new estimator, we conduct a numerical study using four real data sets and a simulation study. The result shows that the suggested estimator has a minimum mean square error and higher percentage relative efficiency as compared to all the existing estimators. These findings demonstrate the significance of our suggested estimator and highlight its potential applications in various fields. Theoretical and numerical analyses show that our suggested estimator outperforms all existing estimators in terms of efficiency. This demonstrates the practical value of incorporating auxiliary variables into the estimation process and the potential for future research in this area.

Optimizing Variance Estimation in Stratified Random Sampling through a Log-Type Estimator for Finite Populations

“…Further, ref. [25] introduced an improved variance estimator and proved its efficiency with others by using three datasets. Ref.…”