Comparer des méthodes

	M-estimateurs (Régression Robuste)	Régression Ridge
Domaine	Statistique	Apprentissage automatique
Famille	Regression model	Machine learning
Année d'origine	2009	1970
Auteur d'origine	Peter J. Huber	Hoerl, A.E. & Kennard, R.W.
Type	Robust linear regression	L2-regularized linear regression
Source fondatrice	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Apparentées	5	4
Résumé	M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.	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.