Σύγκριση μεθόδων

	Robust Regression	Παλινδρόμηση Ridge
Πεδίο	Στατιστική	Μηχανική Μάθηση
Οικογένεια	Regression model	Machine learning
Έτος προέλευσης	1964	1970
Δημιουργός	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Hoerl, A.E. & Kennard, R.W.
Τύπος	Regression with outlier resistance	L2-regularized linear regression
Θεμελιώδης πηγή	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Εναλλακτικές ονομασίες	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Συναφείς	6	4
Σύνοψη	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.