Comparer des méthodes

	Régression linéaire simple robuste	Régression linéaire multiple robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1964-1987	1964–1980s
Auteur d'origine	Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
Type	Robust linear regression	Robust linear regression
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. Annals of Mathematical Statistics, 35(1), 73–101.
Alias	robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
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 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.