Methoden vergelijken

	Generaliseerde Additieve Modellen (GAM)	Kwantielregressie
Vakgebied	Machine learning	Econometrie
Familie	Machine learning	Regression model
Jaar van ontstaan	1986	1978
Grondlegger	Trevor Hastie & Robert Tibshirani	Koenker & Bassett
Type	Semi-parametric additive regression model	Conditional quantile regression
Oorspronkelijke bron	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.
Aliassen	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	conditional quantile regression, regression quantiles, Kantil Regresyon
Verwant	4	5
Samenvatting	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.