Comparer des méthodes

	Régression linéaire simple robuste	Régression quantile
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1964-1987	1978
Auteur d'origine	Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)	Koenker & Bassett
Type	Robust linear regression	Conditional quantile regression
Source fondatrice	Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	6	5
Résumé	Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret.	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.