So sánh phương pháp

	Hồi quy tuyến tính bội vững chắc	Ridge Regression
Lĩnh vực	Thống kê	Học máy
Họ	Regression model	Machine learning
Năm ra đời	1964–1980s	1970
Người khởi xướng	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna	Hoerl, A.E. & Kennard, R.W.
Loại	Robust linear regression	L2-regularized linear regression
Công trình gốc	Huber, P. J. (1964). Robust estimation of a location parameter. 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.
Tên gọi khác	robust MLR, M-estimator regression, resistant multiple regression, robust OLS	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Liên quan	6	4
Tóm tắt	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.	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.