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BEGIN:VEVENT
DTSTAMP:20220111T215428Z
DTSTART:20220126T140000Z
DTEND:20220126T150000Z
SUMMARY:Ed Cohen - Wavelet spectra for multivariate point processes.
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}ul0-ku8fgnc
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DESCRIPTION:Ed Cohen\, Senior Lecturer in Statistics at the Department of
Mathematics\, Imperial College London is our speaker for the Statistics
seminar series.\n\nTitle: Wavelet spectra for multivariate point proces
ses.\n\nAbstract: Humans are harvesting vast event datasets that manifes
t themselves as a list of times at which particular events of interest o
ccur. Often these are multivariate in nature\, with events being of diff
erent types or arriving on multiple channels. A key question is to what
extent the data-generating point processes are correlated and to track n
on-stationary correlation structure. Wavelets provide the flexibility to
analyse stochastic processes at different scales in a time-localised ma
nner and have had a profound impact in statistics\, particularly in time
series analysis. Here\, we apply them to multivariate point processes a
s a means of detecting and analysing unknown non-stationarity\, both wit
hin and across component processes. To provide statistical tractability\
, a temporally smoothed wavelet periodogram is developed and distributio
nal results are extended to wavelet coherence\; a time-scale measure of
inter-process correlation. This statistical framework is further 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 characterise time-varying dependency patterns.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Zoom link: https://zoom.us/j/92947173491
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