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| Régression Gamma (MGL)× | Régression quantile× | |
|---|---|---|
| Domaine≠ | Statistique | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1989 | 1978 |
| Auteur d'origine≠ | McCullagh & Nelder (GLM framework) | Koenker & Bassett |
| Type≠ | Generalized linear model | Conditional quantile regression |
| Source fondatrice≠ | McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias | gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM) | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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