Comparer des méthodes

	Modèle robuste à zéros inflationnistes	Régression robuste par binomiale négative
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1990s–2000s	2000s–2011
Auteur d'origine	Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors	Hilbe, J. M.; Zeileis, A. et al.
Type	Robust count regression with excess zeros	Count regression with robust inference
Source fondatrice	Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. ISBN: 978-0521198158
Alias	robust ZIP, robust ZINB, outlier-resistant zero-inflated regression, robust zero-inflated Poisson	robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression
Apparentées	5	6
Résumé	The robust zero-inflated model extends standard zero-inflated count regression — which handles excess zeros via a mixture of a point mass at zero and a count distribution — by replacing or supplementing classical maximum likelihood with robust estimation techniques (M-estimators, sandwich standard errors) that protect against the distorting influence of outlying observations.	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.