Salīdzināt metodes

	Zero-Inflated Negative Binomial Regression	Puasona un negatīvās binomiālās regresijas
Nozare	Statistika	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1994	1998
Autors	Greene (1994)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tips	Count regression (mixture model)	Generalized linear model for count data
Pirmavots	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.
Citi nosaukumi	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
Saistītās	5	4
Kopsavilkums	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.