Comparer des méthodes

	Modèle à inflation de zéros	Régression de Poisson et binomiale négative
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1992	1998
Auteur d'origine	Diane Lambert	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Count regression with excess zeros	Generalized linear model for count data
Source fondatrice	Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	6	4
Résumé	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.	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.