Mean estimation using robust quantile regression with two auxiliary variables

In the presence of outliers in the data set, the utilization of robust regression tools for mean estimation is a widely established practice in survey sampling with single auxiliary variable. Recently, with the aid of some non-conventional location measures and traditional Ordinary Least Square (OLS), proposed a class of mean estimators using information on two supplementary variates under a simple random sampling framework. The utilization of non-traditional measures of location, especially in the presence of outliers, performed better than existing conventional estimators. In this study, a new class of estimators of mean utilizing quantile regression is proposed. The general forms of Mean Square Error (MSE) and Minimum Mean Square Error (MMSE) are also derived. The theoretical findings are being reinforced by different real-life data sets and simulation study. (c) 2023 Sharif University of Technology. All rights reserved.