Σύγκριση μεθόδων

	M-Εκτιμητές (Εύρωστη Παλινδρόμηση)	Εκτίμηση MM για Ανθεκτική Παλινδρόμηση
Πεδίο	Στατιστική	Στατιστική
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2009	1987
Δημιουργός	Peter J. Huber	Victor J. Yohai
Τύπος	Robust linear regression	Robust linear regression
Θεμελιώδης πηγή	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.
Εναλλακτικές ονομασίες	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici
Συναφείς	5	5
Σύνοψη	M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to 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.