Comparer des méthodes

	Régression robuste par binomiale négative	Régression binomiale négative
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	2000s–2011	2011
Auteur d'origine	Hilbe, J. M.; Zeileis, A. et al.	Hilbe (textbook treatment); generalized linear model framework
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	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Alias	robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression	NB regression, NB2 regression, negatif binom regresyonu
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.	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.