Linganisha mbinu

	Usajili wa Poisson wa Imara	Usuli wa Regresi ya Binomiali Hasiri
Nyanja	Takwimu	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2004	2011
Mwanzilishi	Guangyong Zou	Hilbe (textbook treatment); generalized linear model framework
Aina	GLM with robust variance	Generalized linear model for count data
Chanzo asilia	Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Majina mbadala	modified Poisson regression, Poisson regression with robust standard errors, log-binomial alternative, sandwich-variance Poisson	NB regression, NB2 regression, negatif binom regresyonu
Zinazohusiana	5	4
Muhtasari	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.	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.