Comparer des méthodes

	Régression robuste par binomiale négative	Modèle à inflation de zéros
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2000s–2011	1992
Auteur d'origine	Hilbe, J. M.; Zeileis, A. et al.	Diane Lambert
Type	Count regression with robust inference	Count regression with excess zeros
Source fondatrice	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. ISBN: 978-0521198158	Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14.
Alias	robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression	ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial
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.	A zero-inflated model is a two-component mixture regression designed for count outcomes that contain more zero values than a standard Poisson or negative binomial distribution can accommodate. One component is a binary process that generates structural zeros; the other is a count process that generates both zeros and positive counts.