So sánh phương pháp

	M-Estimators (Hồi quy Mạnh mẽ)	Ridge Regression
Lĩnh vực	Thống kê	Học máy
Họ	Regression model	Machine learning
Năm ra đời	2009	1970
Người khởi xướng	Peter J. Huber	Hoerl, A.E. & Kennard, R.W.
Loại	Robust linear regression	L2-regularized linear regression
Công trình gốc	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Tên gọi khác	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Liên quan	5	4
Tóm tắt	M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.	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.