Abstract
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.
Item Type: | Paper |
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Keywords: | L2Boosting, P-splines, smoothing splines, additive models, variable selection, component-wise base-learners |
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Technical Reports |
URN: | urn:nbn:de:bvb:19-epub-2057-7 |
Language: | English |
Item ID: | 2057 |
Date Deposited: | 30. Oct 2007 |
Last Modified: | 04. Nov 2020, 12:46 |