Comparer des méthodes

	Modèle linéaire généralisé robuste	Régression logistique robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2001	2001
Auteur d'origine	Cantoni & Ronchetti	Cantoni & Ronchetti (2001); Bondell (2008)
Type	Robust regression model	Robust generalized linear model (binary outcome)
Source fondatrice	Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley. ISBN: 978-0470027264	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.
Alias	robust GLM, GLM with robust estimation, robust quasi-likelihood model, M-estimator GLM	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon
Apparentées	5	5
Résumé	A Robust Generalized Linear Model fits the standard GLM family — linear, logistic, Poisson, and others — using M-type estimating equations that down-weight outlying or influential observations. The result is coefficient estimates and standard errors that remain stable even when a minority of data points deviate sharply from the assumed distribution.	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).