Comparer des méthodes

	Modèle Probit Robuste	Régression logistique robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1934 / 1980s	2001
Auteur d'origine	Hal White (sandwich variance); classical probit by Bliss (1934)	Cantoni & Ronchetti (2001); Bondell (2008)
Type	Binary outcome regression with robust inference	Robust generalized linear model (binary outcome)
Source fondatrice	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.
Alias	probit with robust standard errors, sandwich-SE probit, heteroscedasticity-robust probit, M-estimation probit	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon
Apparentées	4	5
Résumé	The Robust Probit Model estimates the probability of a binary outcome using the probit link function while protecting inference from misspecification of the error distribution or heteroscedasticity. Coefficients are obtained via maximum likelihood; standard errors are then replaced by the sandwich (Huber-White) estimator, which remains consistent even when the assumed error variance is incorrect.	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).