Vertaile menetelmiä

	Huber-regressio	MM-estimaattori vankalle regressiolle
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1964	1987
Kehittäjä	Peter J. Huber	Victor J. Yohai
Tyyppi	Robust linear regression (M-estimation)	Robust linear regression
Alkuperäislähde	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.
Rinnakkaisnimet	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici
Liittyvät	5	5
Tiivistelmä	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.	The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.