Quantile regression-ratio-type estimators for mean estimation under complete and partial auxiliary information

Abstract

Traditional ordinary least square (OLS) regression is commonly utilized to develop regressionratio-type estimators with traditional measures of location. Abid et al. (2016b) extended this idea and developed regression-ratio-type estimators with traditional and non-traditional measures of location. In this article, the quantile regression with traditional and non-traditional measures of location is utilized and a class of ratio type mean estimators are proposed. The theoretical mean square error (MSE) expressions are also derived. The work is also extended for two phase sampling (partial information). The pertinence of the proposed and existing group of estimators is shown by considering real data collections originating from different sources. The discoveries are empowering and prevalent execution of the proposed group of estimators is witnessed and documented throughout the article.