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

Abstract

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.

KEYWORDS:

• Robust quantile regression
• Mean square error (MSE)
• Auxiliary variable
• Relative Efficiency

Mathematics Subject Classification::

• 62D05

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgement

Authors are thankful to the Editor-in-Chief and learned referees for their inspiring and fruitful suggestions. The authors are also thankful to the National Institute of Technology, Arunachal Pradesh, Jote, for providing the necessary infrastructure for the completion of the present work.