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Bayesian Model Search for Nonstationary Periodic Time Series

We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behavior. 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 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 behavior 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. Supplementary materials for this article are available online.

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Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION [JASA]; Vol.115 No.531 September 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 1320 - 1335

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

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Subjek

CHANGE-POINTS
REVERSIBLE-JUMP MCMC
BAYESIAN SPECTRAL ANALYSIS
SLEEP APNEA
ULTRADIAN SLEEP CYCLES

Info Detail Spesifik

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Pernyataan Tanggungjawab

Beniamino Hadj-Amar, Bärbel Finkenstädt Rand, Mark Fiecas, Francis Levi, Robert Huckstepp

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