Exponential method of estimation in sampling theory under robust quantile regression methods

• Published in: Communications in statistics. Theory and methods Vol. 53; no. 17; pp. 6285 - 6298
• Main Authors: Yadav, Vinay Kumar, Prasad, Shakti
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 01-09-2024; Taylor & Francis Ltd
• Subjects: Auxiliary variable; Data analysis; Estimators; Mean square error (MSE); Outliers (statistics); Quantiles; Regression analysis; Relative Efficiency; Robust quantile regression; Robustness
• ISSN: 0361-0926; 1532-415X
• DOI: 10.1080/03610926.2023.2243529

Abstract-In the regression analysis, ordinary least square techniques is commonly used. However, the data's outcomes may be untrustworthy if there is an outliers in it. In order to deal with the outliers problem, robust quantile regression methods have been frequently presented as alternatives to OLS for a long time. In this article, primarily a exponential ratio-type estimators is suggested. After that, robust quantile regression estimators are proposed, that is a useful strategy. The application of robust quantile regression empowered the efficiency of the estimators especially for outliers in the data. The MSE equations of the various estimators are computed and compared to OLS approaches. Numerical illustration and simulations studies are performed to support our theoretical findings.