Comparer des méthodes

	Régression logistique robuste	Estimation MM pour la régression robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2001	1987
Auteur d'origine	Cantoni & Ronchetti (2001); Bondell (2008)	Victor J. Yohai
Type	Robust generalized linear model (binary outcome)	Robust linear regression
Source fondatrice	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.
Alias	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici
Apparentées	5	5
Résumé	Robust Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008).	The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.