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Bayesian model search for nonstationary periodic time series
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Hadj-Amar, Beniamino, Finkenstädt, Bärbel, Fiecas, Mark, Lévi, Francis A. and Huckstepp, Robert T. R. (2020) Bayesian model search for nonstationary periodic time series. Journal of the American Statistical Association, 115 (531). pp. 1320-1335. doi:10.1080/01621459.2019.1623043 ISSN 0162-1459.
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WRAP-bayesian-model-search-nonstationary-periodic-time-series-Finkenstadt-Levi-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1967Kb) | Preview |
Official URL: https://doi.org/10.1080/01621459.2019.1623043
Abstract
We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behaviour. We approximate the time series using a piecewise oscillatory model with unknown periodicities, where our goal is to estimate the change-points while simultaneously identifying the potentially changing periodicities in the data. Our proposed methodology is based on a trans-dimensional Markov chain Monte Carlo (MCMC) algorithm that simultaneously updates the change-points and the periodicities relevant to any segment between them. We show that the proposed methodology successfully identifies time changing oscillatory behaviour in two applications which are relevant to e-Health and sleep research, namely the occurrence of ultradian oscillations in human skin temperature during the time of night rest, and the detection of instances of sleep apnea in plethysmographic respiratory traces.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics R Medicine > RC Internal medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Markov processes , Monte Carlo method, Pattern recognition systems, Sleep apnea syndromes | ||||||||
Journal or Publication Title: | Journal of the American Statistical Association | ||||||||
Publisher: | American Statistical Association | ||||||||
ISSN: | 0162-1459 | ||||||||
Official Date: | 2020 | ||||||||
Dates: |
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Volume: | 115 | ||||||||
Number: | 531 | ||||||||
Page Range: | pp. 1320-1335 | ||||||||
DOI: | 10.1080/01621459.2019.1623043 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 28/05/2019, available online: http://www.tandfonline.com/10.1080/01621459.2019.1623043 | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 23 May 2019 | ||||||||
Date of first compliant Open Access: | 28 May 2020 | ||||||||
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