Comparer des méthodes

	Régression linéaire multiple robuste	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1964–1980s	2019
Auteur d'origine	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna	Wooldridge (textbook treatment); classical least squares
Type	Robust linear regression	Linear regression
Source fondatrice	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
Alias	robust MLR, M-estimator regression, resistant multiple regression, robust OLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	6	5
Résumé	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).