Abbreviated Journal Title
Stat. Sin.
Keywords
log-spectral density; spectral density; wavelets; WAVELET SHRINKAGE; SPLINES; Statistics & Probability
Abstract
The problem of estimating the log-spectrum of a stationary time series by Bayesian shrinkage of empirical wavelet coefficients is studied. A model in the wavelet domain that accounts for distributional properties of the log-periodogram at levels of fine detail and approximate normality at coarse levels in the wavelet decomposition, is proposed. The smoothing procedure, called BAMS-LP (Bayesian Adaptive Multiscale Shrinker of Log-Periodogram), ensures that the reconstructed log-spectrum is sufficiently noise-free. It is also shown that the resulting Bayes estimators are asymptotically optimal (in the mean-squared error sense). Comparisons with non-wavelet and wavelet-non-Bayesian methods are discussed.
Journal Title
Statistica Sinica
Volume
17
Issue/Number
2
Publication Date
1-1-2007
Document Type
Article; Proceedings Paper
Language
English
First Page
635
Last Page
666
WOS Identifier
ISSN
1017-0405
Recommended Citation
Pensky, Marianna; Vidakovic, Brani; and Canditiis, Daniela De, "Bayesian decision theoretic scale-adaptive estimation of a log-spectral density" (2007). Faculty Bibliography 2000s. 7518.
https://stars.library.ucf.edu/facultybib2000/7518
Comments
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