Fractal connectivity of long-memory networks

被引：109

作者：

Achard, Sophie ^{[1
,2
,3
]}

Bassett, Danielle S. ^{[1
,2
,4
,5
]}

Meyer-Lindenberg, Andreas ^{[4
]}

Bullmore, Edward T. ^{[1
,2
]}

机构：

[1] Univ Cambridge, Brain Mapping Unit, Cambridge CB2 2QQ, England

[2] Univ Cambridge, Behav & Clin Neurosci Inst, Cambridge CB2 2QQ, England

[3] CNRS, UMR 5216, GIPSA Lab, Grenoble, France

[4] NIMH, NIH, Bethesda, MD 20892 USA

[5] Univ Cambridge, Cavendish Lab, Dept Phys, Cambridge CB3 0HE, England

来源：

PHYSICAL REVIEW E | 2008年 / 77卷 / 03期

基金：

英国惠康基金; 英国医学研究理事会;

关键词：

D O I：

10.1103/PhysRevE.77.036104

中图分类号：

O35 [流体力学]; O53 [等离子体物理学];

学科分类号：

070204 [等离子体物理]; 080103 [流体力学]; 080704 [流体机械及工程];

摘要：

Using the multivariate long memory (LM) model and Taylor expansions, we find the conditions for convergence of the wavelet correlations between two LM processes on an asymptotic value at low frequencies. These mathematical results, and a least squares estimator of LM parameters, are validated in simulations and applied to neurophysiological (human brain) and financial market time series. Both brain and market systems had multivariate LM properties including a "fractal connectivity" regime of scales over which wavelet correlations were invariantly close to their asymptotic value. This analysis provides efficient and unbiased estimation of long-term correlations in diverse dynamic networks.

引用

页数：12

共 36 条

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Wavelet analysis of long-range-dependent traffic [J].