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

DOI10.1214/17-BJPS364zbMath1385.62025arXiv1606.08292MaRDI QIDQ1705542

Jouchi Nakajima, Mike West

Publication date: 16 March 2018

Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1606.08292

zbMATH Keywords

impulse responsemultivariate time seriestime-series decompositiondynamic factor modelsfactor-augmented vector autoregressionEEG time seriesdynamic sparsitysparse time-varying loadingstransfer response factor models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Biomedical imaging and signal processing (92C55)

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Bayesian Computation in Dynamic Latent Factor Models, Bayesian Approaches to Shrinkage and Sparse Estimation, Adaptive variable selection for sequential prediction in multivariate dynamic models, Efficient Bayesian PARCOR approaches for dynamic modeling of multivariate time series, Bayesian emulation for multi-step optimization in decision problems, Sparse Bayesian time-varying covariance estimation in many dimensions, Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior, Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions

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