Comparer des méthodes

	Régression robuste par binomiale négative	Modèle Linéaire Généralisé (GLM)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2000s–2011	1972
Auteur d'origine	Hilbe, J. M.; Zeileis, A. et al.	John A. Nelder & Robert W. M. Wedderburn
Type	Count regression with robust inference	Regression framework
Source fondatrice	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. ISBN: 978-0521198158	Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.
Alias	robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression	GLM, generalized regression, exponential family regression, link-function model
Apparentées	6	6
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.	The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case.