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Schmid, Matthias and Hothorn, Torsten (2007): Boosting Additive Models using Component-wise P-Splines. Department of Statistics: Technical Reports, No.2 [PDF, 483kB]


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.

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