16. Projection Pursuit Regression

  1. Michael G. Schimek
  1. Sigbert Klinke1 and
  2. Janet Grassmann2

Published Online: 30 JAN 2012

DOI: 10.1002/9781118150658.ch16

Smoothing and Regression: Approaches, Computation, and Application

Smoothing and Regression: Approaches, Computation, and Application

How to Cite

Klinke, S. and Grassmann, J. (2000) Projection Pursuit Regression, in Smoothing and Regression: Approaches, Computation, and Application (ed M. G. Schimek), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118150658.ch16

Editor Information

  1. Karl-Franzens-University of Graz, Austria, and University of Vienna, Austria

Author Information

  1. 1

    Humboldt-Universität zu Berlin, Berlin, Germany

  2. 2

    SONEM, Würzburg, Germany

Publication History

  1. Published Online: 30 JAN 2012
  2. Published Print: 24 JUL 2000

ISBN Information

Print ISBN: 9780471179467

Online ISBN: 9781118150658

SEARCH

Keywords:

  • approximation quality;
  • convergence rates;
  • modifications;
  • neural networks;
  • optimization methods

Summary

This chapter contains sections titled:

  • Introduction

  • The Basic PPR Algorithm

  • Quality of Approximation

  • Number of Terms to Choose

  • Interpretable PPR

  • Convergence Rates

  • Modifications

  • PPR and Neural Networks

  • Optimization Methods for PPR and Neural Networks

  • The Implementation of PPR in S-PLUS R, and Xplore

  • An Example

  • Conclusion