Comparer des méthodes

	Estimateur Tau (τ) de régression	Estimateur S pour la régression robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1988	1984
Auteur d'origine	Yohai & Zamar	Rousseeuw & Yohai (1984)
Type	Robust linear regression	Robust linear regression
Source fondatrice	Yohai, V. J., & Zamar, R. H. (1988). High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale. Journal of the American Statistical Association, 83(402), 406-413.	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.
Alias	tau regression estimator, robust tau regression, Tau-Tahmin Edici	S-estimation, robust S-regression, S-Tahmin Edici
Apparentées	4	5
Résumé	The Tau estimator is a robust linear regression method introduced by Yohai and Zamar in 1988 that fits the model by minimising an efficient τ-scale of the residuals. It builds on the scale estimate of the S-estimator to combine a high breakdown point with high statistical efficiency, and is often used as an alternative to the MM-estimator in small samples.	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.