A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling.

Abstract

In this article, we proposed an improved finite population variance estimator based on simple random sampling using dual auxiliary information. Mathematical expressions of the proposed and existing estimators are obtained up to the first order of approximation. Two real data sets are used to examine the performances of a new improved proposed estimator. A simulation study is also recognized to assess the robustness and generalizability of the proposed estimator. From the result of real data sets and simulation study, it is examining that the proposed estimator give minimum mean square error and percentage relative efficiency are higher than all existing counterparts, which shown the importance of new improved estimator. The theoretical and numerical result illustrated that the proposed variance estimator based on simple random sampling using dual auxiliary information has the best among all existing estimators.