A New Class of Quantile Regression Ratio-Type Estimators for Finite Population Mean in Stratified Random Sampling

Abstract

Quantile regression is one of the alternative regression techniques used when the assumptions of classical regression analysis are not met, and it estimates the values of the study variable in various quantiles of the distribution. This study proposes ratio-type estimators of a population mean using the information on quantile regression for stratified random sampling. The proposed ratio-type estimators are investigated with the help of the mean square error equations. Efficiency comparisons between the proposed estimators and classical estimators are presented in certain conditions. Under these obtained conditions, it is seen that the proposed estimators outperform the classical estimators. In addition, the theoretical results are supported by a real data application.

Keywords

• quantile regression
• ratio-type estimators
• mean square error
• efficiency
• stratified random sampling