Сравнение методов

	Zero-Inflated Negative Binomial Regression	Регрессия отрицательного биномиального распределения
Область	Статистика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1994	2011
Автор метода	Greene (1994)	Hilbe (textbook treatment); generalized linear model framework
Тип	Count regression (mixture model)	Generalized linear model for count data
Основополагающий источник	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.
Другие названия	ZINB, ZINB regression, zero-inflated negative binomial model, Sıfır-Şişirilmiş Negatif Binom Regresyonu (ZINB)	NB regression, NB2 regression, negatif binom regresyonu
Связанные	5	4
Сводка	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.