Anderson, Rachele; Sandsten, Maria Inference for time-varying signals using locally stationary processes. (English) Zbl 1407.62309 J. Comput. Appl. Math. 347, 24-35 (2019). Summary: Locally Stationary Processes (LSPs) in Silverman’s sense, defined by the modulation in time of a stationary covariance function, are valuable in stochastic modelling of time-varying signals. However, for practical applications, methods to conduct reliable parameter inference from measured data are required. In this paper, we address the lack of suitable methods for estimating the parameters of the LSP model, by proposing a novel inference method. The proposed method is based on the separation of the two factors defining the LSP covariance function, in order to take advantage of their individual structure and divide the inference problem into two simpler sub-problems. The method’s performance is tested in a simulation study and compared with traditional sample covariance based estimation. An illustrative example of parameter estimation from EEG data, measured during a memory encoding task, is provided. Cited in 2 Documents MSC: 62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH) 60G10 Stationary stochastic processes 62M15 Inference from stochastic processes and spectral analysis 62P10 Applications of statistics to biology and medical sciences; meta analysis 94A12 Signal theory (characterization, reconstruction, filtering, etc.) Keywords:locally stationary process; time-varying signals; time-series modelling; statistical inference; covariance estimation; EEG signals PDFBibTeX XMLCite \textit{R. Anderson} and \textit{M. Sandsten}, J. Comput. Appl. Math. 347, 24--35 (2019; Zbl 1407.62309) Full Text: DOI References: [1] Cramér, H.; Leadbetter, M. 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