Inference for time-varying signals using locally stationary processes

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Matematik
Originalspråkengelska
Sidor (från-till)24-35
TidskriftJournal of Computational and Applied Mathematics
Volym347
StatusPublished - 2019 feb 1
PublikationskategoriForskning
Peer review utfördJa