Reproducing kernel Hilbert spaces for penalized regression: A tutorial

Alvaro Nosedal-Sanchez, Curtis B. Storlie, Thomas C.M. Lee, Ronald Christensen

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Penalized regression procedures have become very popular ways to estimate complicated functions. The smoothing spline, for example, is the solution of a minimization problem in a functional space. If such a minimization problem is posed on a reproducing kernel Hilbert space (RKHS), the solution is guaranteed to exist, is unique, and has a very simple form. There are excellent books and articles about RKHS and their applications in statistics; however, this existing literature is very dense. This article provides a friendly reference for a reader approaching this subject for the first time. It begins with a simple problem, a system of linear equations, and then gives an intuitive motivation for reproducing kernels. Armed with the intuition gained from our first examples, we take the reader from vector spaces to Ba-nach spaces and to RKHS. Finally, we present some statistical estimation problems that can be solved using the mathematical machinery discussed. After reading this tutorial, the reader will be ready to study more advanced texts and articles about the subject, such as those by Wahba or Gu. Online supplements are available for this article.

Original languageEnglish (US)
Pages (from-to)50-60
Number of pages11
JournalAmerican Statistician
Volume66
Issue number1
DOIs
StatePublished - Jun 27 2012

Keywords

  • Projection principle
  • Regularization
  • Representation theorem
  • Ridge regression
  • Smoothing splines

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty

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