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| Regressione Gamma (GLM)× | Regressione di Poisson e Binomiale Negativa× | |
|---|---|---|
| Campo≠ | Statistica | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1989 | 1998 |
| Ideatore≠ | McCullagh & Nelder (GLM framework) | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Tipo≠ | Generalized linear model | Generalized linear model for count data |
| Fonte seminale≠ | McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Alias≠ | gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM) | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Correlati | 4 | 4 |
| Sintesi≠ | Gamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagh and Nelder (1989), it is an alternative to ordinary linear regression for variables such as health-care costs, durations, and income. | 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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