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Activity Number: 634 - Bayesian Methodology
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #328901 Presentation
Title: Bayesian Spectral Analysis of High-Dimensional Time Series
Author(s): Ori Rosen* and Rob Krafty
Companies: Univ of Texas at El Paso and University of Pittsburgh
Keywords: Factor Model; MCMC; Spectral Analysis

We propose a frequency-domain factor model that allows for complex-valued spectra which means that individual time series can propagate in a lagged fashion. Our model allows for different dynamics across the variates of the time series. The spectrum of the factors is assumed smooth as a function of frequency. The real and imaginary parts of the loadings matrix are modeled by tensor products. Inference is performed by MCMC methods, and the method is illustrated with biomedical data.

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

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