A wavelet-based Bayesian approach to regression models with long memory errors and its application to FMRI data.

Volume: 69, Issue: 1, Pages: 184 - 196
Published: Mar 1, 2013
Abstract
This article considers linear regression models with long memory errors. These models have been proven useful for application in many areas, such as medical imaging, signal processing, and econometrics. Wavelets, being self-similar, have a strong connection to long memory data. Here we employ discrete wavelet transforms as whitening filters to simplify the dense variance-covariance matrix of the data. We then adopt a Bayesian approach for the...
Paper Details
Title
A wavelet-based Bayesian approach to regression models with long memory errors and its application to FMRI data.
Published Date
Mar 1, 2013
Journal
Volume
69
Issue
1
Pages
184 - 196
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