Сравнение методов

	Робастная линейная регрессия	Регрессия Хубера
Область	Машинное обучение	Статистика
Семейство	Machine learning	Regression model
Год появления	1964–1987	1964
Автор метода	Huber, P. J.; Rousseeuw, P. J.	Peter J. Huber
Тип	Outlier-resistant supervised regression	Robust linear regression (M-estimation)
Основополагающий источник	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.
Другие названия	robust regression, M-estimator regression, Huber regression, outlier-resistant regression	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu
Связанные	5	5
Сводка	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.