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Bayesian Computation in Dynamic Latent Factor Models

Publication ,  Journal Article
Lavine, I; Cron, A; West, M
Published in: Journal of Computational and Graphical Statistics
January 1, 2022

Bayesian computation for filtering and forecasting analysis is developed for a broad class of dynamic models. The ability to scale-up such analyses in non-Gaussian, nonlinear multivariate time series models is advanced through the introduction of a novel copula construction in sequential filtering of coupled sets of dynamic generalized linear models. The new copula approach is integrated into recently introduced multiscale models in which univariate time series are coupled via nonlinear forms involving dynamic latent factors representing cross-series relationships. The resulting methodology offers dramatic speed-up in online Bayesian computations for sequential filtering and forecasting in this broad, flexible class of multivariate models. Two examples in nonlinear models for very heterogeneous time series of nonnegative counts demonstrate massive computational efficiencies relative to existing, simulation-based methods, while defining similar filtering and forecasting outcomes.

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Published In

Journal of Computational and Graphical Statistics

DOI

EISSN

1537-2715

ISSN

1061-8600

Publication Date

January 1, 2022

Volume

31

Issue

3

Start / End Page

651 / 665

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

APA
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Lavine, I., Cron, A., & West, M. (2022). Bayesian Computation in Dynamic Latent Factor Models. Journal of Computational and Graphical Statistics, 31(3), 651–665. https://doi.org/10.1080/10618600.2021.2021208
Lavine, I., A. Cron, and M. West. “Bayesian Computation in Dynamic Latent Factor Models.” Journal of Computational and Graphical Statistics 31, no. 3 (January 1, 2022): 651–65. https://doi.org/10.1080/10618600.2021.2021208.
Lavine I, Cron A, West M. Bayesian Computation in Dynamic Latent Factor Models. Journal of Computational and Graphical Statistics. 2022 Jan 1;31(3):651–65.
Lavine, I., et al. “Bayesian Computation in Dynamic Latent Factor Models.” Journal of Computational and Graphical Statistics, vol. 31, no. 3, Jan. 2022, pp. 651–65. Scopus, doi:10.1080/10618600.2021.2021208.
Lavine I, Cron A, West M. Bayesian Computation in Dynamic Latent Factor Models. Journal of Computational and Graphical Statistics. 2022 Jan 1;31(3):651–665.

Published In

Journal of Computational and Graphical Statistics

DOI

EISSN

1537-2715

ISSN

1061-8600

Publication Date

January 1, 2022

Volume

31

Issue

3

Start / End Page

651 / 665

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 1403 Econometrics
  • 0104 Statistics