Comparer des méthodes

	Modèle Probit Robuste	Régression Robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1934 / 1980s	1964
Auteur d'origine	Hal White (sandwich variance); classical probit by Bliss (1934)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Type	Binary outcome regression with robust inference	Regression with outlier resistance
Source fondatrice	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Alias	probit with robust standard errors, sandwich-SE probit, heteroscedasticity-robust probit, M-estimation probit	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Apparentées	4	6
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 regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.