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Bayesian automatic polynomial wavelet regression |
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| Authors: | Hee-Seok Oh Hyoung-Moon Kim |
| Institution: | aDepartment of Statistics, Seoul National University, Seoul 151-747, Republic of Korea bDepartment of Applied Statistics, Konkuk University, Seoul 143-701, Republic of Korea |
| 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. |
| Keywords: | Boundary problem Empirical Bayes MCMC Polynomial regression Wavelet regression |
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