Comparer des méthodes

	Régression binomiale négative à zéro inflation (ZINB)	Modèle à seuil pour données de comptage
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1994	1986
Auteur d'origine	Greene (1994)	Mullahy
Type	Count regression (mixture model)	Two-part count model
Source fondatrice	Greene, W. H. (1994). Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. NYU Working Paper.	Mullahy, J. (1986). Specification and Testing of Some Modified Count Data Models. Journal of Econometrics, 33(3), 341–365.
Alias	ZINB, ZINB regression, zero-inflated negative binomial model, Sıfır-Şişirilmiş Negatif Binom Regresyonu (ZINB)	hurdle count model, two-part count model, zero-truncated count model, Engel Modeli (Hurdle Model)
Apparentées	5	5
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.	The hurdle model is a two-part count-data model introduced by Mullahy (1986). A first stage models the binary choice of crossing a hurdle (a zero versus a non-zero count), and a second stage models the strictly positive counts with a zero-truncated distribution such as a zero-truncated Poisson or negative binomial.