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dc.contributor.authorPensky, Marianna
dc.contributor.authorVidakovic, Brani
dc.date.accessioned2008-12-08T18:53:41Z
dc.date.available2008-12-08T18:53:41Z
dc.date.issued2003
dc.identifier.urihttp://hdl.handle.net/1853/25912
dc.description.abstractThe problem of estimating the log-spectrum of a stationary Gaussian time series by Bayesianly induced 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 as noise-free as possible. It is also shown that the resulting Bayes estimators are asymptotically optimal (in the frequentist sense). Comparisons with non-wavelet and wavelet-non-Bayesian methods are discussed.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.relation.ispartofseriesBiomedical Engineering Technical Report ; 01/2003en
dc.subjectSpectral densityen
dc.subjectLog-spectral densityen
dc.subjectWaveletsen
dc.titleBayesian Decision Theoretic Scale-Adaptive Estimation of a Log-Spectral Densityen
dc.typeTechnical Reporten


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