Comparer des méthodes

	Régression Ridge Robuste	Régression Robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1991	1964
Auteur d'origine	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Type	Regularized robust linear regression	Regression with outlier resistance
Source fondatrice	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. The Annals of Mathematical Statistics, 35(1), 73–101.
Alias	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Apparentées	5	6
Résumé	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 regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.