Dynamics & sparsity in latent threshold factor models: A study in multivariate EEG signal processing
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.
Duke Scholars
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- 4905 Statistics
- 4901 Applied mathematics
- 0104 Statistics
- 0103 Numerical and Computational Mathematics
- 0102 Applied Mathematics
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Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- 4905 Statistics
- 4901 Applied mathematics
- 0104 Statistics
- 0103 Numerical and Computational Mathematics
- 0102 Applied Mathematics