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Activity Number:
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585
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
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Sponsor:
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International Society for Bayesian Analysis (ISBA)
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Abstract #318397
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View Presentation
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Title:
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Adaptive Spectral Analysis of Replicated Nonstationary Time Series
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Author(s):
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Robert Krafty* and Martica Hall and Scott Bruce
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Companies:
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University of Pittsburgh and University of Pittsburgh and Temple University
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Keywords:
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Replicated Time Series ;
Whittle Likelihood ;
Spectral Analysis ;
Reversible Jump MCMC ;
Smoothing Spline
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Abstract:
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Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of the time series and other study outcomes. However, the time-varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. In this talk, we discuss a new method that offers simultaneous and automatic estimation and inference on the association between the time-varying spectrum and cross-sectional variables. A locally-stationary model is employed whereby local spectra are indexed by time and variable and estimated using smoothing splines. A reversible jump Markov chain Monte Carlo sampling procedure allows for the number and location of indices to change from one iteration to the next. The approach provides a flexible and adaptive spectral estimate that can automatically capture both smooth and abrupt changes. The proposed methodology is used to analyze the association between the time-varying spectrum of heart rate variability and self-reported sleep quality in a study of older adults serving as the primary caregiver for their ill spouse.
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Authors who are presenting talks have a * after their name.
