So sánh phương pháp

	Hồi quy tuyến tính bội vững chắc	Hồi quy Bình phương Tối thiểu Thông thường (OLS)
Lĩnh vực	Thống kê	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	1964–1980s	2019
Người khởi xướng	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna	Wooldridge (textbook treatment); classical least squares
Loại	Robust linear regression	Linear regression
Công trình gốc	Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Tên gọi khác	robust MLR, M-estimator regression, resistant multiple regression, robust OLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liên quan	6	5
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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).