Comparer des méthodes

	Régression de Huber	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	Peter J. Rousseeuw
Type	Robust linear regression (M-estimation)	Robust linear regression
Source fondatrice	Huber, P. J. (1964). Robust Estimation of a Location Parameter. 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	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression
Apparentées	5	5
Résumé	Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit.	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.