Comparer des méthodes

	Régression par Moindres Carrés Ordinaires (MCO)	Estimation Robuste de la Covariance (MCD)
Domaine	Économétrie	Statistique
Famille	Regression model	Regression model
Année d'origine	2019	1999
Auteur d'origine	Wooldridge (textbook treatment); classical least squares	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)
Type	Linear regression	Robust multivariate location-scatter estimator
Source fondatrice	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.
Alias	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)
Apparentées	5	4
Résumé	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).	Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation.