Comparer des méthodes

	Modèle robuste à zéros inflationnistes	Régression de Poisson robuste
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1990s–2000s	2004
Auteur d'origine	Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors	Guangyong Zou
Type	Robust count regression with excess zeros	GLM with robust variance
Source fondatrice	Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25.	Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706.
Alias	robust ZIP, robust ZINB, outlier-resistant zero-inflated regression, robust zero-inflated Poisson	modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson
Apparentées	5	5
Résumé	The robust zero-inflated model extends standard zero-inflated count regression — which handles excess zeros via a mixture of a point mass at zero and a count distribution — by replacing or supplementing classical maximum likelihood with robust estimation techniques (M-estimators, sandwich standard errors) that protect against the distorting influence of outlying observations.	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.