Сравнение методов

	Пуассоновская регрессия с избытком нулей (Zero-Inflated Poisson, ZIP)	Zero-Inflated Negative Binomial Regression
Область	Статистика	Статистика
Семейство	Regression model	Regression model
Год появления	1992	1994
Автор метода	Diane Lambert	Greene (1994)
Тип	Count regression (two-component mixture)	Count regression (mixture model)
Основополагающий источник	Lambert, D. (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics, 34(1), 1–14.	Greene, W. H. (1994). Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. NYU Working Paper.
Другие названия	ZIP regression, zero-inflated count model, Sıfır-Şişirilmiş Poisson Regresyonu (ZIP)	ZINB, ZINB regression, zero-inflated negative binomial model, Sıfır-Şişirilmiş Negatif Binom Regresyonu (ZINB)
Связанные	4	5
Сводка	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.	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.