1. Proceedings
  2. I3M
  3. 2019
  4. EMSS
  5. 10.46354/i3m.2019.emss.015

Model selection method for efficient signals processing from discrete data

  • a Evgeny A. Pchelintsev  ,
  • b Serguei M. Pergamenshchikov  
  • a,b International Laboratory of Statistics of Stochastic Processes and Quantitative Finance, Tomsk State University, 36 Lenin Avenue, Tomsk, Russia
  • b Laboratory of Mathematics Raphael Salem, University of Rouen Normandy, 12 Avenue de l’Universite, Saint Etienne du Rouvray, France
Cite as
Pchelintsev E.A., Pergamenshchikov S.M.  (2019). Model selection method for efficient signals processing from discrete data. Proceedings of the 31st European Modeling & Simulation Symposium (EMSS 2019), pp. 90-95. DOI: https://doi.org/10.46354/i3m.2019.emss.015.

Abstract

The paper considers the problem of robust adaptive efficient estimating of a periodic signal modeled by a continuous time regression model with the dependent noises given by a non-Gaussian Ornstein-Uhlenbeck process with Levy subordinator in the case when continuous observation cannot be provided and only discrete time measurements are available. Adaptive model selection procedure, based on the improved weighted least square estimates, is proposed. Under some conditions on the noise distribution, sharp oracle inequality for the robust risk has been proved and the robust efficiency of the model selection procedure has been established. The numerical analysis results are given.

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