Salīdzināt metodes

	Zero-Inflated Negative Binomial Regression	Negatīvā binomiālā regresija
Nozare	Statistika	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1994	2011
Autors	Greene (1994)	Hilbe (textbook treatment); generalized linear model framework
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.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Citi nosaukumi	ZINB, ZINB regression, zero-inflated negative binomial model, Sıfır-Şişirilmiş Negatif Binom Regresyonu (ZINB)	NB regression, NB2 regression, negatif binom regresyonu
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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.