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Testing for time-localized coherence in bivariate data

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Testing for time-localized coherence in bivariate data. / Sheppard, L. W.; Stefanovska, A.; McClintock, P. V. E.
In: Physical Review E, Vol. 85, No. 4, 046205, 09.04.2012, p. -.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Sheppard, LW, Stefanovska, A & McClintock, PVE 2012, 'Testing for time-localized coherence in bivariate data', Physical Review E, vol. 85, no. 4, 046205, pp. -. https://doi.org/10.1103/PhysRevE.85.046205

APA

Sheppard, L. W., Stefanovska, A., & McClintock, P. V. E. (2012). Testing for time-localized coherence in bivariate data. Physical Review E, 85(4), -. Article 046205. https://doi.org/10.1103/PhysRevE.85.046205

Vancouver

Sheppard LW, Stefanovska A, McClintock PVE. Testing for time-localized coherence in bivariate data. Physical Review E. 2012 Apr 9;85(4):-. 046205. doi: 10.1103/PhysRevE.85.046205

Author

Sheppard, L. W. ; Stefanovska, A. ; McClintock, P. V. E. / Testing for time-localized coherence in bivariate data. In: Physical Review E. 2012 ; Vol. 85, No. 4. pp. -.

Bibtex

@article{7b2d620b12264c90a52d2eaa05315993,
title = "Testing for time-localized coherence in bivariate data",
abstract = "We present a method for the testing of significance when evaluating the coherence of two oscillatory time series that may have variable amplitude and frequency. It is based on evaluating the self-correlations of the time series. We demonstrate our approach by the application of wavelet-based coherence measures to artificial and physiological examples. Because coherence measures of this kind are strongly biased by the spectral characteristics of the time series, we evaluate significance by estimation of the characteristics of the distribution of values that may occur due to chance associations in the data. The expectation value and standard deviation of this distribution are shown to depend on the autocorrelations and higher order statistics of the data. Where the coherence value falls outside this distribution, we may conclude that there is a causal relationship between the signals regardless of their spectral similarities or differences.",
author = "Sheppard, {L. W.} and A. Stefanovska and McClintock, {P. V. E.}",
note = "{\textcopyright}2012 American Physical Society",
year = "2012",
month = apr,
day = "9",
doi = "10.1103/PhysRevE.85.046205",
language = "English",
volume = "85",
pages = "--",
journal = "Physical Review E",
issn = "1539-3755",
publisher = "American Physical Society",
number = "4",

}

RIS

TY - JOUR

T1 - Testing for time-localized coherence in bivariate data

AU - Sheppard, L. W.

AU - Stefanovska, A.

AU - McClintock, P. V. E.

N1 - ©2012 American Physical Society

PY - 2012/4/9

Y1 - 2012/4/9

N2 - We present a method for the testing of significance when evaluating the coherence of two oscillatory time series that may have variable amplitude and frequency. It is based on evaluating the self-correlations of the time series. We demonstrate our approach by the application of wavelet-based coherence measures to artificial and physiological examples. Because coherence measures of this kind are strongly biased by the spectral characteristics of the time series, we evaluate significance by estimation of the characteristics of the distribution of values that may occur due to chance associations in the data. The expectation value and standard deviation of this distribution are shown to depend on the autocorrelations and higher order statistics of the data. Where the coherence value falls outside this distribution, we may conclude that there is a causal relationship between the signals regardless of their spectral similarities or differences.

AB - We present a method for the testing of significance when evaluating the coherence of two oscillatory time series that may have variable amplitude and frequency. It is based on evaluating the self-correlations of the time series. We demonstrate our approach by the application of wavelet-based coherence measures to artificial and physiological examples. Because coherence measures of this kind are strongly biased by the spectral characteristics of the time series, we evaluate significance by estimation of the characteristics of the distribution of values that may occur due to chance associations in the data. The expectation value and standard deviation of this distribution are shown to depend on the autocorrelations and higher order statistics of the data. Where the coherence value falls outside this distribution, we may conclude that there is a causal relationship between the signals regardless of their spectral similarities or differences.

U2 - 10.1103/PhysRevE.85.046205

DO - 10.1103/PhysRevE.85.046205

M3 - Journal article

VL - 85

SP - -

JO - Physical Review E

JF - Physical Review E

SN - 1539-3755

IS - 4

M1 - 046205

ER -