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Activity Number: 585
Type: Invited
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #318397 View Presentation
Title: Adaptive Spectral Analysis of Replicated Nonstationary Time Series
Author(s): Robert Krafty* and Martica Hall and Scott Bruce
Companies: University of Pittsburgh and University of Pittsburgh and Temple University
Keywords: Replicated Time Series ; Whittle Likelihood ; Spectral Analysis ; Reversible Jump MCMC ; Smoothing Spline

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.

Authors who are presenting talks have a * after their name.

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