Comparer des méthodes

	Régression robuste par binomiale négative	Régression de Poisson robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2000s–2011	2004
Auteur d'origine	Hilbe, J. M.; Zeileis, A. et al.	Guangyong Zou
Type	Count regression with robust inference	GLM with robust variance
Source fondatrice	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. ISBN: 978-0521198158	Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706.
Alias	robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression	modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson
Apparentées	6	5
Résumé	Robust Negative Binomial Regression models overdispersed count outcomes using the negative binomial distribution while protecting coefficient inference against misspecification of the variance function. It pairs maximum-likelihood estimation of the mean and dispersion parameters with sandwich (Huber-White) standard errors, yielding valid tests even when the assumed variance structure is only approximately correct.	Robust Poisson regression fits a Poisson log-linear model to a binary outcome but replaces the model-based variance with the empirical sandwich estimator. This yields valid standard errors and risk ratios even though Poisson variance assumptions are technically violated for binary data. The approach, popularized by Zou (2004), is widely used in epidemiology as a numerically stable alternative to log-binomial regression.