Comparer des méthodes

	Moindres Carrés Pondérés Bayésiens (Bayesian WLS)	Moindres Carrés Pondérés Robustes (Robust WLS)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1971	1964/1981
Auteur d'origine	Arnold Zellner (Bayesian econometrics framework)	Huber, P. J.
Type	Bayesian weighted regression	Robust weighted regression
Source fondatrice	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley, New York. ISBN: 978-0471169376	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054
Alias	Bayesian weighted regression, BWLS, Bayesian heteroscedastic regression, weighted Bayesian linear regression	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression
Apparentées	4	5
Résumé	Bayesian Weighted Least Squares combines the classical WLS weighting scheme — which downweights observations with high error variance — with Bayesian prior distributions over the regression coefficients and error variance. The result is a posterior distribution that reflects both the data likelihood and prior beliefs, providing full uncertainty quantification in heteroscedastic settings.	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.