Abstract Details
Activity Number:
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421
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Type:
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Contributed
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Date/Time:
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Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #315898
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View Presentation
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Title:
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Bayesian Spectral Analysis of Replicated Multiple Time Series with Multiple Covariates
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Author(s):
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Ori Rosen* and Robert Krafty and Sally Wood
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Companies:
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The University of Texas at El Paso and Temple University and The University of Sydney Business School
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Keywords:
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MCMC ;
Multivariate Time Series ;
Smoothing Splines ;
Spectral Analysis ;
Whittle Likelihood
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Abstract:
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We propose a frequency-domain model for fitting multiple time series collected on subjects along with covariates. The underlying spectral matrix is modeled as a function of both frequency and covariates using a mixture formulation. The multivariate Whittle approximation is used for approximating the likelihood, where the spectral matrix is expressed as a modified Cholesky decomposition. Smoothing splines are used for estimating the elements of the spectral matrix as a function of frequency. The proposed method is applied to sleep research data.
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Authors who are presenting talks have a * after their name.
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