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| Generalisiertes Additives Modell (GAM)× | Polynomiale Regression× | |
|---|---|---|
| Fachgebiet≠ | Maschinelles Lernen | Statistik |
| Familie≠ | Machine learning | Regression model |
| Entstehungsjahr≠ | 1986 | 2012 |
| Urheber≠ | Trevor Hastie & Robert Tibshirani | Montgomery, Peck & Vining (textbook treatment); classical least squares |
| Typ≠ | Semi-parametric additive regression model | Linear regression in transformed predictors |
| Wegweisende Quelle≠ | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 |
| Aliasnamen≠ | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model | polynomial least squares, curvilinear regression, Polinom Regresyonu |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | 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. | Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends. |
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