Comparer des méthodes

	Régression Robuste	Régression par Moindres Carrés Trimés (LTS)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1964	1984
Auteur d'origine	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Peter J. Rousseeuw
Type	Regression with outlier resistance	Robust linear regression
Source fondatrice	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.
Alias	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	LTS, least trimmed squares regression, trimmed least squares, robust regression
Apparentées	6	5
Résumé	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	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.