Σύγκριση μεθόδων

	Παλινδρόμηση Huber	Εκτίμηση MM για Ανθεκτική Παλινδρόμηση
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1964	1987
Δημιουργός	Peter J. Huber	Victor J. Yohai
Τύπος	Robust linear regression (M-estimation)	Robust linear regression
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici
Συναφείς	5	5
Σύνοψη	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.