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Journal Article

Robust statistical detection of power-law cross-correlation

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Blythe, D. A. J., Nikulin, V. V., & Müller, K.-R. (2016). Robust statistical detection of power-law cross-correlation. Scientific Reports, 6: 27089. doi:10.1038/srep27089.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002D-3C22-7
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.