Mean estimation using robust quantile regression with two auxiliary variables

Document Type : Article

Authors

1 - Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan - Department of Mathematics and Statistics - PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan

2 Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan

3 - 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

4 Department of Mathematics, College of Science, Mustansiriyah University, Baghdad 10011, Iraq

5 Department of Mathematics and Statistics - PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan

Abstract

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. Abid et al. (2018),
with the aid of some non-conventional location measures and traditional 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, we have proposed a new class of
estimators 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.

Keywords

References

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Scientia Iranica
Volume 30, Issue 3
Transactions on Industrial Engineering (E)
May and June 2023
Pages 1245-1254

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