Article ID Journal Published Year Pages File Type
417172 Computational Statistics & Data Analysis 2008 14 Pages PDF
Abstract

An efficient approximation of L2L2 Boosting with component-wise smoothing splines is considered. 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. A detailed analysis of the effect of various P-spline hyper-parameters on the boosting fit is given. In addition, a new theoretical result on the relationship between the boosting stopping iteration and the step length factor used for shrinking the boosting estimates is derived.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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