Wavelet Spectra for Multivariate Point Processes

Cohen, Edward and Gibberd, Alex (2021) Wavelet Spectra for Multivariate Point Processes. Biometrika. ISSN 0006-3444

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Wavelets provide the flexibility to analyse stochastic processes at different scales. Here, we apply them to multivariate point processes as a means of detecting and analysing unknown non-stationarity, both within and across data streams. To provide statistical tractability, a temporally smoothed wavelet periodogram is developed and shown to be equivalent to a multi-wavelet periodogram. Under a stationary assumption, the distribution of the temporally smoothed wavelet periodogram is demonstrated to be asymptotically Wishart, with the centrality matrix and degrees of freedom readily computable from the multi-wavelet formulation. Distributional results extend to wavelet coherence; a time-scale measure of inter-process correlation. This statistical framework is used to construct a test for stationarity in multivariate point-processes. The methodology is applied to neural spike train data, where it is shown to detect and characterize time-varying dependency patterns.

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This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated versionE A K Cohen, A J Gibberd, Wavelet Spectra for Multivariate Point Processes, Biometrika, 2021;, asab054, https://doi.org/10.1093/biomet/asab054 is available online at:
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25 Nov 2021 15:20
Last Modified:
04 May 2022 02:29