Comparer des méthodes

	Régression linéaire simple robuste	Régression Robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1964-1987	1964
Auteur d'origine	Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Type	Robust linear regression	Regression with outlier resistance
Source fondatrice	Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Alias	robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Apparentées	6	6
Résumé	Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret.	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.