Performance of some new robust ridge estimators

被引：1
|
作者：
Suhail, Muhammad [1 ,2 ]
Layouni, Zoubir [3 ]
机构：
[1] Amir Muhammad Khan Campus, Dept Stat, Mardan, Pakistan
[2] Univ Agr, Peshawar, Pakistan
[3] Badji Mokhtar Univ, Dept Comp Sci, Annaba, Algeria
来源：
2022 14TH INTERNATIONAL CONFERENCE ON MATHEMATICS, ACTUARIAL SCIENCE, COMPUTER SCIENCE AND STATISTICS (MACS) | 2022年
关键词：
M-estimator; MSE; Multicollinearity; Outliers; OLS; RR; REGRESSION;
D O I：
10.1109/MACS56771.2022.10022657
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Multicollinearity among predictors distorts the performance of ordinary least squared estimators. This performance further deteriorates when outliers are also present in the data. Robust ridge regression has been widely used to circumvent this joint problem. In this paper, some new robust ridge estimators based on m-estimators are proposed. The new estimators have been compared with existing ones through Monte Carlo simulation. The simulation results showed that new estimators outperform other considered estimators by considering the criteria of mean squared error. An application has also been presented to support the best performance of new estimators.
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页数：5
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