方法对比

	Robust Ridge Regression	稳健多元线性回归
领域	统计学	统计学
方法族	Regression model	Regression model
起源年份	1991	1964–1980s
提出者	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
类型	Regularized robust linear regression	Robust linear regression
开创性文献	Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333.	Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.
别名	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
相关	5	6
摘要	Robust Ridge regression combines M-estimation with L2 (ridge) regularization to produce coefficient estimates that are simultaneously resistant to outliers and stable under multicollinearity. It minimizes a robust loss function (such as Huber's) penalized by the squared norm of the coefficient vector, downweighting influential observations while shrinking correlated predictors toward zero.	Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients.