Boosting additive models using component-wise P-Splines
Copyright policy )Boosting additive models using component-wise P-Splines
We consider an efficient approximation of Bühlmann & Yu’s L2Boosting algorithm with component-wise smoothing splines. Smoothing spline base-learners are replaced by P-spline base-learners which yield similar prediction errors but are more advantageous from a computational point of view. In particular, we give a detailed analysis on the effect of various P-spline hyper-parameters on the boosting fit. In addition, we derive a new theoretical result on the relationship between the boosting stopping iteration and the step length factor used for shrinking the boosting estimates.
L2Boosting, P-splines, smoothing splines, additive models, variable selection, component-wise base-learners, Computational problems in statistics, Nonparametric regression and quantile regression
L2Boosting, P-splines, smoothing splines, additive models, variable selection, component-wise base-learners, Computational problems in statistics, Nonparametric regression and quantile regression
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