Comparer des méthodes

	Régression quantile robuste	Modèle linéaire généralisé robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1993–1997	2001
Auteur d'origine	Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)	Cantoni & Ronchetti
Type	Robust semiparametric regression	Robust regression model
Source fondatrice	Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275	Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley. ISBN: 978-0470027264
Alias	robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR	robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM
Apparentées	6	5
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.	A Robust Generalized Linear Model fits the standard GLM family — linear, logistic, Poisson, and others — using M-type estimating equations that down-weight outlying or influential observations. The result is coefficient estimates and standard errors that remain stable even when a minority of data points deviate sharply from the assumed distribution.