Comparer des méthodes

	Régression robuste par binomiale négative	Régression de Poisson et binomiale négative
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	2000s–2011	1998
Auteur d'origine	Hilbe, J. M.; Zeileis, A. et al.	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Count regression with robust inference	Generalized linear model for count data
Source fondatrice	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. ISBN: 978-0521198158	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	6	4
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.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.