Statistical inference and time-frequency estimation for non-stationary signal classification

Rachele Anderson

Research output: ThesisDoctoral Thesis (compilation)

25 Downloads (Pure)


This thesis focuses on statistical methods for non-stationary signals. The methods considered or developed address problems of stochastic modeling, inference, spectral analysis, time-frequency analysis, and deep learning for classification. In all the contributions, an example of a biomedical application of the proposed method is provided, either to electroencephalography (EEG) data or to Heart Rate Variability (HRV) data. Four manuscripts are included in this Ph.D. thesis.
Original languageEnglish
Awarding Institution
  • Mathematical Statistics
  • Sandsten, Maria, Supervisor
Award date2019 Oct 4
Place of PublicationLund, Sweden
ISBN (Print)978-91-7895-274-8
ISBN (electronic) 978-91-7895-275-5
Publication statusPublished - 2019 Sept 9

Bibliographical note

Defence details
Date: 2019-10-04
Time: 09:15
Place: Lecture hall MH:R, Matematikcentrum, Sölvegatan 18A, Lund
External reviewer(s)
Name: Baxevani, Anastassia
Title: Docent
Affiliation: University of Cyprus, Nicosia, Cyprus

Subject classification (UKÄ)

  • Signal Processing
  • Probability Theory and Statistics

Free keywords

  • Non-stationary processes
  • stochastic modeling
  • inference
  • spectral analysis
  • time-frequency analysis
  • classification
  • biomedical applications
  • deep learning


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