Automatic statistical analysis of bivariate nonstationary time series (In memory of Jonathan A. Raz). (English) Zbl 1018.62080
Summary: We propose a new method for analyzing bivariate nonstationary time series. The proposed method is a statistical procedure that automatically segments the time series into approximately stationary blocks and selects the span to be used to obtain the smoothed estimates of the time-varying spectra and coherence. It is based on the smooth localized complex exponential (SLEX) transform, which forms a library of orthogonal complex-valued transforms that are simultaneously localized in time and frequency. We show that the smoothed SLEX periodograms are consistent estimators, report simulation results, and apply the method to a two-channel electroencephalogram dataset recorded during an epileptic seizure.
MSC:
62M15 | Inference from stochastic processes and spectral analysis |
62P10 | Applications of statistics to biology and medical sciences; meta analysis |
62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |