Searls estimation strategy for population mean of a sensitive study variable harnessing non-sensitive auxiliary information

Abstract

In this study, we present a Searls type regression estimator for elevated estimation of the population mean of a sensitive study variable in the presence of a known non-sensitive supplementary variable under the simple random sampling scheme. The first order of approximation is used to obtain the bias and mean square error expressions. The suggested family of estimators is compared to competing estimators both theoretically and numerically. The findings verified through the real and simulated data show that the suggested estimator is preferably chosen over many of the existing competing estimators.