Inference for time-varying signals using locally stationary processes

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Abstract

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.

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  • Mathematics
Original languageEnglish
Pages (from-to)24-35
JournalJournal of Computational and Applied Mathematics
Volume347
Publication statusPublished - 2019 Feb 1
Publication categoryResearch
Peer-reviewedYes