Comparer des méthodes

	Estimateur S pour la régression robuste	Régression quantile
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1984	1978
Auteur d'origine	Rousseeuw & Yohai (1984)	Koenker & Bassett
Type	Robust linear regression	Conditional quantile regression
Source fondatrice	Rousseeuw, P. J. & Yohai, V. J. (1984). Robust Regression by Means of S-Estimators. In Robust and Nonlinear Time Series Analysis (Lecture Notes in Statistics, Vol. 26, pp. 256-272). Springer.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	S-estimation, robust S-regression, S-Tahmin Edici	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	5	5
Résumé	The S-estimator is a robust linear-regression method, introduced by Rousseeuw and Yohai in 1984, that estimates the coefficients by minimising a robust M-estimate of the residual scale rather than the variance of the residuals. By driving down a bounded measure of residual spread it can attain a breakdown point of up to 50%, so it stays reliable even when a large share of the data are outliers, and it provides the first stage of the well-known MM-estimator.	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.