Сравнение на методи

	Модел с бариера за данни с брой	Негативно-биномна регресия	Регресия на Поасон и отрицателна биномна регресия
Област	Статистика	Иконометрия	Иконометрия
Семейство	Regression model	Regression model	Regression model
Година на възникване	1986	2011	1998
Създател	Mullahy	Hilbe (textbook treatment); generalized linear model framework	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Тип	Two-part count model	Generalized linear model for count data	Generalized linear model for count data
Основополагащ източник	Mullahy, J. (1986). Specification and Testing of Some Modified Count Data Models. Journal of Econometrics, 33(3), 341–365.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Други названия	hurdle count model, two-part count model, zero-truncated count model, Engel Modeli (Hurdle Model)	NB regression, NB2 regression, negatif binom regresyonu	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Свързани	5	4	4
Резюме	The hurdle model is a two-part count-data model introduced by Mullahy (1986). A first stage models the binary choice of crossing a hurdle (a zero versus a non-zero count), and a second stage models the strictly positive counts with a zero-truncated distribution such as a zero-truncated Poisson or negative binomial.	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.	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.