Compara mètodes

	Regressió de Huber	Regressió per Mínims Quadrats Troncats (LTS)
Camp	Estadística	Estadística
Família	Regression model	Regression model
Any d'origen	1964	1984
Autor original	Peter J. Huber	Peter J. Rousseeuw
Tipus	Robust linear regression (M-estimation)	Robust linear regression
Font seminal	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.
Àlies	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression
Relacionats	5	5
Resum	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.