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dc.contributor.authorNason, G
dc.contributor.authorStevens, Kara
dc.date.accessioned2017-08-31T07:49:55Z
dc.date.available2017-08-31T07:49:55Z
dc.date.issued2015-09-18
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.otherARTN e0137662
dc.identifier.urihttp://hdl.handle.net/10026.1/9882
dc.description.abstract

It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant electrocardiogram data. A major additional benefit of the Bayesian paradigm is that we obtain rigorous and useful credible intervals of the evolving spectral structure. We show how the Bayesian credible intervals provide extra insight into the infant electrocardiogram data.

dc.format.extente0137662-e0137662
dc.format.mediumElectronic-eCollection
dc.languageen
dc.language.isoeng
dc.publisherPublic Library of Science (PLoS)
dc.subjectBayes Theorem
dc.subjectBrain
dc.subjectComputer Simulation
dc.subjectElectroencephalography
dc.subjectHumans
dc.subjectWavelet Analysis
dc.titleBayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram
dc.typejournal-article
dc.typeJournal Article
dc.typeResearch Support, Non-U.S. Gov't
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000361790200031&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue9
plymouth.volume10
plymouth.publication-statusPublished online
plymouth.journalPLOS ONE
dc.identifier.doi10.1371/journal.pone.0137662
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Institute of Health and Community
dc.publisher.placeUnited States
dcterms.dateAccepted2015-08-19
dc.identifier.eissn1932-6203
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1371/journal.pone.0137662
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2015
rioxxterms.typeJournal Article/Review


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