Mean estimation using robust quantile regression with two auxiliary variables
نویسندگان
1 Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan
2 Department of Mathematics, College of Science, Mustansiriyah University, Baghdad 10011, Iraq
3 Department of Mathematics and Statistics - PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan
4 - Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan - Department of Mathematics and Statistics - PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan
5 - Department of Mathematics, College of Science, King Khalid University, Abha 62529, Saudi Arabia - Statistical Research and Studies Support Unit, King Khalid University, Abha 62529, Saudi Arabia
doi
10.24200/sci.2022.57170.5098چکیده
In the presence of outliers in the data set, the utilization of robust regression tools for mean estimationis a widely established practice in survey sampling with single auxiliary variable. Abid et al. (2018),with the aid of some non-conventional location measures and traditional OLS, proposed a class of meanestimators 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, we have proposed a new class ofestimators of mean utilizing quantile regression. The general forms of MSE and MMSE are also derived.The theoretical findings are being reinforced by different real-life data sets and simulation study.
