Comparer des méthodes

	Régression par Moindres Carrés Trimés (LTS)	M-estimateurs (Régression Robuste)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1984	2009
Auteur d'origine	Peter J. Rousseeuw	Peter J. Huber
Type	Robust linear regression	Robust linear regression
Source fondatrice	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.
Alias	LTS, least trimmed squares regression, trimmed least squares, robust regression	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler
Apparentées	5	5
Résumé	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.	M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.