Comparer des méthodes

	Estimation Robuste de la Covariance (MCD)	Estimation par écart absolu médian (MAD)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1999	1974
Auteur d'origine	Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)	Hampel (influence-curve treatment); classical robust statistics
Type	Robust multivariate location-scatter estimator	Robust scale estimator
Source fondatrice	Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.
Alias	minimum covariance determinant, MCD estimator, robust covariance estimation, Robust Kovaryans Tahmini (MCD)	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini
Apparentées	4	5
Résumé	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.	Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result.