Online Program Home
My Program

Abstract Details

Activity Number: 233 - Innovative Approaches for High-Dimensional Omics and Neuroimaging Data
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: International Indian Statistical Association
Abstract #304564
Title: Efficient Approaches for Dynamic Modeling of Multivariate Time Series
Author(s): Raquel Prado*
Companies: UC Santa Cruz-Baskin School of Engineering
Keywords: Multivariate time series ; Non-stationary time series ; Multivariate PARCOR models

The partial correlation function (PARCOR) provides a characterization of time series processes. We present a Bayesian PARCOR modeling approach for fast and accurate inference in multivariate non-stationary time series settings. Our formulation models the forward and backward PARCOR coefficients of a multivariate time series process using multivariate dynamic linear models. We obtain computationally efficient approximate inference of the variance-covariance matrices and the multivariate time-varying PARCOR coefficients. Approximate inference on the implied time-varying vector autoregressive (TV-VAR) coefficients can also be obtained using Whittle's algorithm. Similarly, approximate inference on functions of these parameters such as the multivariate time-frequency spectra, coherence, and partial coherence can also be obtained. A key aspect of the PARCOR approach is that it requires multivariate DLM representations of lower dimension than those required by commonly used multivariate non-stationary models such as TV-VARs. The performance of the PARCOR approach is shown in simulations and in the analysis of multivariate temporal data from neurosciences and environmental applications.

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

Back to the full JSM 2019 program