Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1150482 | Journal of Statistical Planning and Inference | 2008 | 10 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Hee-Seok Oh, Hyoung-Moon Kim,