Comparer des méthodes

	Régression quantile robuste	Régression Robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1993–1997	1964
Auteur d'origine	Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Type	Robust semiparametric regression	Regression with outlier resistance
Source fondatrice	Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Alias	robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Apparentées	6	6
Résumé	Robust Quantile Regression estimates conditional quantiles of a response variable while simultaneously downweighting the influence of outliers. By combining the asymmetric loss function of standard quantile regression with bounded-influence or M-estimation weights, it provides reliable quantile estimates even when data contain extreme observations or heavy-tailed error distributions.	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.