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    Modeling dependence via copula of functionals of Fourier coefficients

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    FinalPaper_TESTjournal.pdf
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    Type
    Article
    Authors
    Fontaine, Charles cc
    Frostig, Ron D.
    Ombao, Hernando cc
    KAUST Department
    Biostatistics Group
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2020-03-13
    Embargo End Date
    2021-03-13
    Submitted Date
    2018-09-25
    Permanent link to this record
    http://hdl.handle.net/10754/662174
    
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    Abstract
    The goal of this paper is to develop a measure for characterizing complex dependence between time series that cannot be captured by traditional measures such as correlation and coherence. Our approach is to use copula models of functionals of the Fourier coefficients which is a generalization of coherence. Here, we use standard parametric copula models with a single parameter from both elliptical and Archimedean families. Our approach is to analyze changes in activity in local field potentials in the rat cortex prior to and immediately following the onset of stroke. We present the necessary theoretical background, the multivariate models and an illustration of our methodology on these local field potential data. Simulations with nonlinear dependent data reveal that there is information that is missed by not taking into account dependence on specific frequencies. Moreover, these simulations demonstrate how our proposed method captures more complex nonlinear dependence between time series. Finally, we illustrate our copula-based approach in the analysis of local field potentials of rats.
    Citation
    Fontaine, C., Frostig, R. D., & Ombao, H. (2020). Modeling dependence via copula of functionals of Fourier coefficients. TEST. doi:10.1007/s11749-020-00703-5
    Sponsors
    Hernando Ombao was supported by KAUST Baseline Funds, and Ron D. Frostig was supported by the Leducq Foundation (15CVD02).
    Publisher
    Springer Nature
    Journal
    TEST
    DOI
    10.1007/s11749-020-00703-5
    arXiv
    1809.09448
    Additional Links
    http://link.springer.com/10.1007/s11749-020-00703-5
    https://rdcu.be/b23Li
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11749-020-00703-5
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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