Comparer des méthodes

	Modèle additif généralisé (GAM)	Régression quantile
Domaine	Apprentissage automatique	Économétrie
Famille	Machine learning	Regression model
Année d'origine	1986	1978
Auteur d'origine	Trevor Hastie & Robert Tibshirani	Koenker & Bassett
Type	Semi-parametric additive regression model	Conditional quantile regression
Source fondatrice	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	4	5
Résumé	A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response.	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.