Comparer des méthodes

	Régression logistique robuste	Estimation MM pour la régression robuste	Régression quantile
Domaine	Statistique	Statistique	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	2001	1987	1978
Auteur d'origine	Cantoni & Ronchetti (2001); Bondell (2008)	Victor J. Yohai	Koenker & Bassett
Type	Robust generalized linear model (binary outcome)	Robust linear regression	Conditional quantile 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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
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	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	5	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.