Comparer des méthodes

	Moindres Carrés Pondérés Robustes (Robust WLS)	Moindres Carrés Généralisés Robustes (MCG Robustes)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1964/1981	1936 / 1980
Auteur d'origine	Huber, P. J.	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Type	Robust weighted regression	Robust linear regression
Source fondatrice	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Alias	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Apparentées	5	5
Résumé	Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates.	Robust GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure.