Bandingkan metode

	Regresi Ridge Robust	Regresi Linier Berganda Robust
Bidang	Statistika	Statistika
Keluarga	Regression model	Regression model
Tahun asal	1991	1964–1980s
Pencetus	Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna
Tipe	Regularized robust linear regression	Robust linear regression
Sumber perintis	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.
Alias	ridge M-estimation, robust regularized regression, M-estimator ridge, outlier-resistant ridge regression	robust MLR, M-estimator regression, resistant multiple regression, robust OLS
Terkait	5	6
Ringkasan	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.