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

J Sanderson, PZ Fryzlewicz, MW Jones

Research output: Contribution to journalArticle (Academic Journal)peer-review

28 Citations (Scopus)

Abstract

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.
Translated title of the contributionEstimating linear dependence between non-stationary time series using the locally stationary wavelet model
Original languageEnglish
Pages (from-to)435 - 446
Number of pages12
JournalBiometrika
Volume97
DOIs
Publication statusPublished - Apr 2010

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