Comparer des méthodes
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| Modèle Linéaire Généralisé (GLM)× | Régression binomiale négative× | |
|---|---|---|
| Domaine≠ | Statistique | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1972 | 2011 |
| Auteur d'origine≠ | John A. Nelder & Robert W. M. Wedderburn | Hilbe (textbook treatment); generalized linear model framework |
| Type≠ | Regression framework | Generalized linear model for count data |
| Source fondatrice≠ | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| Alias≠ | GLM, generalized regression, exponential family regression, link-function model | NB regression, NB2 regression, negatif binom regresyonu |
| Apparentées≠ | 6 | 4 |
| Résumé≠ | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. | 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. |
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