Σύγκριση μεθόδων

	Zero-Inflated Poisson Regression	Παλινδρόμηση Poisson και Αρνητική Διωνυμική
Πεδίο	Στατιστική	Οικονομετρία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1992	1998
Δημιουργός	Diane Lambert	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Τύπος	Count regression (two-component mixture)	Generalized linear model for count data
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	ZIP regression, zero-inflated count model, Sıfır-Şişirilmiş Poisson Regresyonu (ZIP)	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Συναφείς	4	4
Σύνοψη	Zero-Inflated Poisson regression is a two-component model for count data that contains more zeros than an ordinary Poisson model can explain. Introduced by Diane Lambert in 1992, it combines a logistic model for the zero-generating mechanism with a Poisson model for the genuine counting process.	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.