Dynamics & sparsity in latent threshold factor models: A study in multivariate EEG signal processing

Journal Article (Journal Article)

We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametriza-tions via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models, extending prior Bayesian modeling of multiple EEG series and highlighting the practical utility of the latent thresholding concept in multivariate, non-stationary time series analysis.

Full Text

Duke Authors

Cited Authors

  • Nakajima, J; West, M

Published Date

  • January 1, 2017

Published In

Volume / Issue

  • 31 / 4

Start / End Page

  • 701 - 731

International Standard Serial Number (ISSN)

  • 0103-0752

Digital Object Identifier (DOI)

  • 10.1214/17-BJPS364

Citation Source

  • Scopus