Comparer des méthodes

	Régression binomiale négative à zéro inflation (ZINB)	Régression de Poisson et binomiale négative
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1994	1998
Auteur d'origine	Greene (1994)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Count regression (mixture model)	Generalized linear model for count data
Source fondatrice	Greene, W. H. (1994). Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. NYU Working Paper.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	ZINB, ZINB regression, zero-inflated negative binomial model, Sıfır-Şişirilmiş Negatif Binom Regresyonu (ZINB)	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	5	4
Résumé	Zero-Inflated Negative Binomial regression is a count model, introduced by Greene (1994), that handles count data showing both an excess of zeros and overdispersion. It combines a binary inflation process that generates structural zeros with a negative binomial count process, making it one of the most widely used distributions for real-world count data.	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.