Comparar métodos

	Regresión Binomial Negativa Robusta	Regresión Binomial Negativa
Campo	Estadística	Econometría
Familia	Regression model	Regression model
Año de origen	2000s–2011	2011
Autor original	Hilbe, J. M.; Zeileis, A. et al.	Hilbe (textbook treatment); generalized linear model framework
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	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Alias	robust NB regression, negative binomial regression with robust standard errors, sandwich-corrected negative binomial regression, NB2 robust regression	NB regression, NB2 regression, negatif binom regresyonu
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.	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.