Comparar métodos

	Regresión Binomial Negativa Robusta	Regresión de Poisson y Binomial Negativa
Campo	Estadística	Econometría
Familia	Regression model	Regression model
Año de origen	2000s–2011	1998
Autor original	Hilbe, J. M.; Zeileis, A. et al.	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tipo	Count regression with robust inference	Generalized linear model for count data
Fuente seminal	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. ISBN: 978-0521198158	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Relacionados	6	4
Resumen	Robust Negative Binomial Regression models overdispersed count outcomes using the negative binomial distribution while protecting coefficient inference against misspecification of the variance function. It pairs maximum-likelihood estimation of the mean and dispersion parameters with sandwich (Huber-White) standard errors, yielding valid tests even when the assumed variance structure is only approximately correct.	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.