方法对比

	Robust Ridge Regression	岭回归（Ridge Regression）
领域	统计学	机器学习
方法族	Regression model	Machine learning
起源年份	1991	1970
提出者	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Hoerl, A.E. & Kennard, R.W.
类型	Regularized robust linear regression	L2-regularized 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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
别名	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
相关	5	4
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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.