Comparar métodos

	Regresión Lineal Robusta	Regresión de Huber
Campo	Aprendizaje automático	Estadística
Familia	Machine learning	Regression model
Año de origen	1964–1987	1964
Autor original	Huber, P. J.; Rousseeuw, P. J.	Peter J. Huber
Tipo	Outlier-resistant supervised regression	Robust linear regression (M-estimation)
Fuente seminal	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.
Alias	robust regression, M-estimator regression, Huber regression, outlier-resistant regression	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu
Relacionados	5	5
Resumen	Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.	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.