Comparer des méthodes

	Régression Ridge Robuste	Régression Elastic Net
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1991	2005
Auteur d'origine	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Hui Zou and Trevor Hastie
Type	Regularized robust linear regression	Penalized linear regression
Source fondatrice	Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333.	Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.
Alias	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression
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.	Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone.