Comparer des méthodes

	Régression linéaire multiple robuste	Régression Ridge
Domaine	Statistique	Apprentissage automatique
Famille	Regression model	Machine learning
Année d'origine	1964–1980s	1970
Auteur d'origine	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna	Hoerl, A.E. & Kennard, R.W.
Type	Robust linear regression	L2-regularized linear regression
Source fondatrice	Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	robust MLR, M-estimator regression, resistant multiple regression, robust OLS	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Apparentées	6	4
Résumé	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.	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.