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| Generaliseret Additiv Model (GAM)× | Kvantilregression× | |
|---|---|---|
| Fagområde≠ | Maskinlæring | Økonometri |
| Familie≠ | Machine learning | Regression model |
| Oprindelsesår≠ | 1986 | 1978 |
| Ophavsperson≠ | Trevor Hastie & Robert Tibshirani | Koenker & Bassett |
| Type≠ | Semi-parametric additive regression model | Conditional quantile regression |
| Oprindelig kilde≠ | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Aliasser≠ | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Relaterede≠ | 4 | 5 |
| Resumé≠ | 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. |
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