Penalized Splines and Reproducing Kernel Methods

2006/07/18 by N. D Pearce, N.D. Pearce, M. P Wand +1 · 6 citations
Computer Science · Engineering · Mathematics · #Image and Signal Denoising Methods #Advanced Numerical Analysis Techniques #Numerical methods in inverse problems

paper · doi:10.1198/000313006x124541

Abstract

Two data analytic research areas—penalized splines and reproducing kernel methods—have become very vibrant since the mid-1990s. This article shows how the former can be embedded in the latter via theory for reproducing kernel Hilbert spaces. This connection facilitates cross-fertilization between the two bodies of research. In particular, connections between support vector machines and penalized splines are established. These allow for significant reductions in computational complexity, and easier incorporation of special structure such as additivity.

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• A free-knot spline modeling framework for piecewise linear logistic regression in complex samples with body mass index and mortality as an example. [europepmc]
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