Bayesian automatic polynomial wavelet regression

  • Oh H
  • Kim H
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Abstract

This paper considers the problem of Bayesian automatic polynomial wavelet regression (PWR). We propose three different Bayesian methods based on integrated likelihood, conditional empirical Bayes, and reversible jump Markov chain Monte Carlo (MCMC). From the simulation results, we find that the proposed methods are similar to or superior to the existing ones. © 2008 Elsevier B.V. All rights reserved.

Author-supplied keywords

  • Boundary problem
  • Empirical Bayes
  • MCMC
  • Polynomial regression
  • Wavelet regression

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Authors

  • Hee Seok Oh

  • Hyoung Moon Kim

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