Robust Statistical Detection of Power-Law Cross-Correlation

Duncan A.J. Blythe, Vadim V. Nikulin, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


We show that widely used approaches in statistical physics incorrectly indicate the existence of power-law cross-correlations between financial stock market fluctuations measured over several years and the neuronal activity of the human brain lasting for only a few minutes. While such cross-correlations are nonsensical, no current methodology allows them to be reliably discarded, leaving researchers at greater risk when the spurious nature of cross-correlations is not clear from the unrelated origin of the time series and rather requires careful statistical estimation. Here we propose a theory and method (PLCC-test) which allows us to rigorously and robustly test for power-law cross-correlations, correctly detecting genuine and discarding spurious cross-correlations, thus establishing meaningful relationships between processes in complex physical systems. Our method reveals for the first time the presence of power-law cross-correlations between amplitudes of the alpha and beta frequency ranges of the human electroencephalogram.

Original languageEnglish
Article number27089
JournalScientific reports
Publication statusPublished - 2016 Jun 2

ASJC Scopus subject areas

  • General


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