Advertisement

Spline Smoothing

  • Bertrand Clarke
  • Ernest Fokoué
  • Hao Helen Zhang
Chapter
Part of the Springer Series in Statistics book series (SSS)

In the kernel methods of Chapter 2, the estimator of a function is defined first and then a measure of precision is invoked to assess how close the estimator is to the true function. In this chapter, this is reversed. The starting point is a precision criterion, and the spline smoother is the result of minimizing it. Before presenting and developing the machinery of smoothing splines, it is worthwhile to introduce interpolating splines. This parallels the discussion of Early smoothers and the considerations leading to local smoothers, thereby giving insight into the formulation of smoothing splines. Like the kernel-based methods of the last chapter, splines suffer from the Curse of Dimensionality. Nevertheless, there is a parallel theory for multivariate splines. Eubank (1988), Chapter 6.2.3 touches on it with some references. Such Laplacian smoothing splines are neglected here, as are partial splines, which generalize splines to include an extra nonparametric component.

Keywords

Hilbert Space Design Point Reproduce Kernel Hilbert Space Kernel Regression Spline Smoothing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  • Bertrand Clarke
    • 1
  • Ernest Fokoué
    • 2
  • Hao Helen Zhang
    • 3
  1. 1.University of MiamiMiamiCanada
  2. 2.Department of Science & MathematicsKettering UniversityFlintUSA
  3. 3.Department of StatisticsNorth Carolina State University Program in Statistical GeneticsRaleighUSA

Personalised recommendations