Comparer des méthodes

	Régression de Poisson robuste	Régression binomiale négative
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	2004	2011
Auteur d'origine	Guangyong Zou	Hilbe (textbook treatment); generalized linear model framework
Type	GLM with robust variance	Generalized linear model for count data
Source fondatrice	Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Alias	modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson	NB regression, NB2 regression, negatif binom regresyonu
Apparentées	5	4
Résumé	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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.