השוואת שיטות

	רגרסיית רכס רובסטית	רגרסיה לינארית מרובת משתנים חסינה
תחום	סטטיסטיקה	סטטיסטיקה
משפחה	Regression model	Regression model
שנת המקור	1991	1964–1980s
הוגה השיטה	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
סוג	Regularized robust linear regression	Robust linear regression
מקור מכונן	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.
כינויים	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
קשורות	5	6
תקציר	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.