Comparer des méthodes

	Moindres Carrés Pondérés (MCP)	Régression Robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1935	1964
Auteur d'origine	Alexander Craig Aitken	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Type	Weighted linear estimator	Regression with outlier resistance
Source fondatrice	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Alias	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Apparentées	3	6
Résumé	Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.	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.