Comparer des méthodes

	Régression Elastic Net	Régression Robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2005	1964
Auteur d'origine	Hui Zou and Trevor Hastie	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Type	Penalized linear regression	Regression with outlier resistance
Source fondatrice	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.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Alias	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Apparentées	6	6
Résumé	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.	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.