Salīdzināt metodes

	Robustā lineārā regresija	Hjūbera regresija
Nozare	Mašīnmācīšanās	Statistika
Saime	Machine learning	Regression model
Izcelsmes gads	1964–1987	1964
Autors	Huber, P. J.; Rousseeuw, P. J.	Peter J. Huber
Tips	Outlier-resistant supervised regression	Robust linear regression (M-estimation)
Pirmavots	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.
Citi nosaukumi	robust regression, M-estimator regression, Huber regression, outlier-resistant regression	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu
Saistītās	5	5
Kopsavilkums	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.