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In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear response variable depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions.
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model smoothing displaystyle gams gam using smooth example methods parameters parameter functions models estimation also generalized function fitting basis term
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| z j f j | instance of | term | 0.80 | text |
| Generalized cross validation | instance of | An alternative is to select the smoothing parameters to optimize a prediction error criterion | 0.80 | text |
| thin plate spline is appropriate | instance of | are naturally on the same scale so that an isotropic smoother | 0.80 | text |
| GLMs may be preferable to GAMs unless GAMs improve predictive ability substantially | instance of | simpler models | 0.80 | text |
| Generalized additive model | related to background | It | 0.60 | section |
| Generalized additive model | related to background | Kolmogorov | 0.60 | section |
| Generalized additive model | related to background | Arnold | 0.60 | section |
| Generalized additive model | related to background | Unfortunately | 0.60 | section |
| Generalized additive model | related to background | Certain | 0.60 | section |
| Generalized additive model | related to background | Therefore | 0.60 | section |
| Generalized additive model | related to Software | Backfit GAMs | 0.60 | section |
| Generalized additive model | related to Software | The SAS | 0.60 | section |
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