Comparer des méthodes

	Régression quantile robuste	Régression quantile
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1993–1997	1978
Auteur d'origine	Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)	Koenker & Bassett
Type	Robust semiparametric regression	Conditional quantile regression
Source fondatrice	Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR	conditional quantile regression, regression quantiles, Kantil Regresyon
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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.