So sánh phương pháp

	Hồi quy tuyến tính bội vững chắc	Hồi quy Lasso
Lĩnh vực	Thống kê	Học máy
Họ	Regression model	Machine learning
Năm ra đời	1964–1980s	1996
Người khởi xướng	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna	Tibshirani, R.
Loại	Robust linear regression	Regularized linear regression (L1 penalty)
Công trình gốc	Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Tên gọi khác	robust MLR, M-estimator regression, resistant multiple regression, robust OLS	LASSO Regresyonu, lasso, L1-regularized regression, L1 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.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.