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Boosting additive models using component-wise P-Splines

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  • Schmid, Matthias
  • Hothorn, Torsten

Abstract

An efficient approximation of L2 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.

Suggested Citation

  • Schmid, Matthias & Hothorn, Torsten, 2008. "Boosting additive models using component-wise P-Splines," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 298-311, December.
    • Handle: RePEc:eee:csdana:v:53:y:2008:i:2:p:298-311
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