6. Spline and Kernel Regression for Dependent Data

  1. Michael G. Schimek
  1. Robert Kohn1,
  2. Michael G. Schimek2 and
  3. Michael Smith3

Published Online: 30 JAN 2012

DOI: 10.1002/9781118150658.ch6

Smoothing and Regression: Approaches, Computation, and Application

Smoothing and Regression: Approaches, Computation, and Application

How to Cite

Kohn, R., Schimek, M. G. and Smith, M. (2000) Spline and Kernel Regression for Dependent Data, in Smoothing and Regression: Approaches, Computation, and Application (ed M. G. Schimek), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118150658.ch6

Editor Information

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

Author Information

  1. 1

    The Australian Graduate School of Management, Sydney, Australia

  2. 2

    Karl-Franzens-Universität Graz, Graz, Austria

  3. 3

    University of Sydney, Sydney, Australia

Publication History

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

ISBN Information

Print ISBN: 9780471179467

Online ISBN: 9781118150658

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Keywords:

  • spline regression;
  • kernel regression;
  • dependent data;
  • known autocorrelation function;
  • unknown autocorrelation function

Summary

This chapter contains sections titled:

  • Introduction

  • Approaches for a Known Autocorrelation Function

  • Approaches for an Unknown Autocorrelation Function

  • A Bayesian Approach to Smoothing Dependent Data

  • Applications of Smoothing Dependent Data