Comparer des méthodes

	Régression linéaire simple robuste	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1964-1987	2019
Auteur d'origine	Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)	Wooldridge (textbook treatment); classical least squares
Type	Robust linear regression	Linear regression
Source fondatrice	Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons. ISBN: 978-0471852339	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	robust SLR, M-estimator simple regression, outlier-resistant simple regression, robust bivariate regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	6	5
Résumé	Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret.	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).