Usporedite metode

	Robust Ridge Regression	Robustna višestruka linearna regresija
Područje	Statistika	Statistika
Obitelj	Regression model	Regression model
Godina nastanka	1991	1964–1980s
Tvorac	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
Vrsta	Regularized robust linear regression	Robust linear regression
Temeljni izvor	Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333.	Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.
Drugi nazivi	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
Srodne	5	6
Sažetak	Robust Ridge regression combines M-estimation with L2 (ridge) regularization to produce coefficient estimates that are simultaneously resistant to outliers and stable under multicollinearity. It minimizes a robust loss function (such as Huber's) penalized by the squared norm of the coefficient vector, downweighting influential observations while shrinking correlated predictors toward zero.	Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients.