Сравнение на методи

	Робастна Поасонова регресия	Регресия на Поасон и отрицателна биномна регресия
Област	Статистика	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2004	1998
Създател	Guangyong Zou	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Тип	GLM with robust variance	Generalized linear model for count data
Основополагащ източник	Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Други названия	modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Свързани	5	4
Резюме	Robust Poisson regression fits a Poisson log-linear model to a binary outcome but replaces the model-based variance with the empirical sandwich estimator. This yields valid standard errors and risk ratios even though Poisson variance assumptions are technically violated for binary data. The approach, popularized by Zou (2004), is widely used in epidemiology as a numerically stable alternative to log-binomial regression.	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.