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

Traditional Ordinary Least Square (OLS) regression is commonly utilized to develop regression-ratio-type estimators through traditional measurement of location. Abid et al. [Abid, M., Abbas, N., Zafar Nazir, H., et al. "Enhancing the mean ratio estimators for estimating population mean using non-conventional location parameters", Revista Colombiana de Estadistica, 39(1), pp. 63-79 (2016b)], extended this idea and developed regression-ratio-type estimators based on 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 is proposed. The theoretical Mean Square Error (MSE) expressions are also derived. The work is also extended to two-phase sampling (partial information). The relationship between the proposed and existing groups 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 the estimators is witnessed and documented throughout the article. (C) 2022 Sharif University of Technology. All rights reserved.