Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Regresión Binomial Negativa× | Regresión de Poisson y Binomial Negativa× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2011 | 1998 |
| Autor original≠ | Hilbe (textbook treatment); generalized linear model framework | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Tipo | Generalized linear model for count data | Generalized linear model for count data |
| Fuente seminal≠ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Alias≠ | NB regression, NB2 regression, negatif binom regresyonu | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Relacionados | 4 | 4 |
| Resumen≠ | 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. |
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