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dc.contributor.authorTavakoli, Shahinen
dc.contributor.authorPanaretos, VMen
dc.date.accessioned2016-01-26T17:01:21Z
dc.date.available2016-01-26T17:01:21Z
dc.date.issued2016-07-02en
dc.identifier.issn0162-1459
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/253493
dc.description.abstract© 2016 The Author(s). Association of American Geographers © Shahin Tavakoli and Victor Panaretos. Motivated by the problem of inferring the molecular dynamics of DNA in solution, and linking them with its base-pair composition, we consider the problem of comparing the dynamics of functional time series (FTS), and of localizing any inferred differences in frequency and along curvelength. The approach we take is one of Fourier analysis, where the complete second-order structure of the FTS is encoded by its spectral density operator, indexed by frequency and curvelength. The comparison is broken down to a hierarchy of stages: at a global level, we compare the spectral density operators of the two FTS, across frequencies and curvelength, based on a Hilbert–Schmidt criterion; then, we localize any differences to specific frequencies; and, finally, we further localize any differences along the length of the random curves, that is, in physical space. A hierarchical multiple testing approach guarantees control of the averaged false discovery rate over the selected frequencies. In this sense, we are able to attribute any differences to distinct dynamic (frequency) and spatial (curvelength) contributions. Our approach is presented and illustrated by means of a case study in molecular biophysics: how can one use molecular dynamics simulations of short strands of DNA to infer their temporal dynamics at the scaling limit, and probe whether these depend on the sequence encoded in these strands? Supplementary materials for this article are available online.
dc.language.isoenen
dc.rightsAttribution 4.0 International
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleDetecting and Localizing Differences in Functional Time Series Dynamics: A Case Study in Molecular Biophysicsen
dc.typeArticle
dc.provenanceOA-6781
prism.endingPage1035
prism.issueIdentifier515en
prism.publicationDate2016en
prism.publicationNameJournal of the American Statistical Associationen
prism.startingPage1020
prism.volume111en
dc.rioxxterms.funderEPSRC
dc.rioxxterms.projectidEP/K021672/2
datacite.cites.urlhttps://www.repository.cam.ac.uk/handle/1810/253695
dcterms.dateAccepted2016-01-22en
rioxxterms.versionofrecord10.1080/01621459.2016.1147355en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2016-07-02en
dc.identifier.eissn1537-274X
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/K021672/2)
cam.issuedOnline2016-10-18en
cam.orpheus.successThu Jan 30 12:55:21 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International