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Estimating linear dependence between nonstationary time series using the locally stationary wavelet model

By Jean Sanderson, Piotr Fryzlewicz and M. W. Jones


Large volumes of neuroscience data comprise multiple, nonstationary electrophysiological or neuroimaging time series recorded from different brain regions. Accurately estimating the dependence between such neural time series is critical, since changes in the dependence structure are presumed to reflect functional interactions between neuronal populations. We propose a new dependence measure, derived from a bivariate locally stationary wavelet time series model. Since wavelets are localized in both time and scale, this approach leads to a natural, local and multi-scale estimate of nonstationary dependence. Our methodology is illustrated by application to a simulated example, and to electrophysiological data relating to interactions between the rat hippocampus and prefrontal cortex during working memory and decision making

Topics: QH301 Biology, HA Statistics
Publisher: Oxford University Press
Year: 2010
DOI identifier: 10.1093/biomet
OAI identifier:
Provided by: LSE Research Online

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