A comparison of some new and old robust ridge regression estimators. Issue 8 (3rd August 2021)

• Record Type: Journal Article
• Authors: Ali, Sajid; Khan, Himmad; Shah, Ismail; Butt, Muhammad Moeen; Suhail, Muhammad
• Is Part Of: Communications in statistics. Volume 50:Issue 8(2021)
• Page Start: 2213
• Page End: 2231
• Publication Date: 2021-08-03
• Subjects: Multicollinearity -- Ridge regression -- Monte Carlo Simulation; 62J07 -- 62G05 -- 62F10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2019.1597119
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: Ridge regression is used to circumvent the problem of multicollinearity among predictors and many estimators for ridge parameter k are available in the literature. However, if the level of collinearity among predictors is high, the existing estimators also have high mean square errors (MSE). In this paper, we consider some existing and propose new estimators for the estimation of ridge parameter k . Extensive Monte Carlo simulations as well as a real-life example are used to evaluate the performance of proposed estimators based on the MSE criterion. The results show the superiority of our proposed estimators compared to the existing estimators.