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dc.contributor.authorFontaine, Charles
dc.contributor.authorFrostig, Ron D.
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2019-09-24T13:32:50Z
dc.date.available2019-09-24T10:03:31Z
dc.date.available2019-09-24T13:32:50Z
dc.date.issued2019-09-09
dc.identifier.citationFontaine, C., Frostig, R. D., & Ombao, H. (2020). Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials. Econometrics and Statistics, 15, 85–103. doi:10.1016/j.ecosta.2019.06.003
dc.identifier.doi10.1016/j.ecosta.2019.06.003
dc.identifier.urihttp://hdl.handle.net/10754/656783
dc.description.abstractTools for characterizing non-linear spectral dependence between spontaneous brain signals are developed, based on the use of parametric copula models (both bivariate and vine models) applied on the magnitude of Fourier coefficients rather than using coherence. The motivation is an experiment on rats that studied the impact of stroke on the connectivity structure (dependence) between local field potentials recorded by various microelectrodes. The following major questions are addressed. The first is to determine changepoints in the regime within a microelectrode for a given frequency band based on a difference between the cumulative distribution functions modeled for each epoch (small window of time). The proposed approach is an iterative algorithm which compares each successive bivariate copulas on all the epochs range, using a bivariate Kolmogorov-Smirnov statistic. The second is to determine if such changes are present only in some microelectrodes versus generalized across the entire network. These issues are addressed by comparing Vine-copulas models fitted for each epoch. The necessary framework is provided and the effectiveness of the methods is shown through the results for the local field potential data analysis of a rat.
dc.description.sponsorshipHernando Ombao was supported by KAUST Baseline Funds and Ron D. Frostig was supported by the Leducq Foundation (15CVD02).
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S2452306219300450
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Econometrics and Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Econometrics and Statistics, [[Volume], [Issue], (2019-09-09)] DOI: 10.1016/j.ecosta.2019.06.003 . © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectChangepoints
dc.subjectDependence
dc.subjectFourier transform
dc.subjectParametric copulas
dc.subjectSpectral domain
dc.subjectVine copulas
dc.titleModeling non-linear spectral domain dependence using copulas with applications to rat local field potentials
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalEconometrics and Statistics
dc.rights.embargodate2021-09-09
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Neurobiology and Behavior, University of California-Irvine, U.S.A.
dc.identifier.arxivid1809.08785
kaust.personFontaine, Charles
kaust.personOmbao, Hernando
refterms.dateFOA2019-09-24T10:04:25Z
kaust.acknowledged.supportUnitKAUST Baseline Funds
dc.date.published-online2019-09-09
dc.date.published-print2019-09
dc.date.posted2018-09-24


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