Dynamic Graphical Models with Variable Selection for Effective Connectivity-Reference-Cited by-同舟云学术

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Dynamic Graphical Models with Variable Selection for Effective Connectivity

Published:2023-01-01 Issue:-1 Volume:-1 Page:
ISSN:1936-0975
Container-title:Bayesian Analysis
language:
Short-container-title:Bayesian Anal.

Author:

Souza Rebecca¹,Costa Lilia²,Paez Marina¹,Sato João³,Barreto Candida⁴

Affiliation:

1. Department of Statistical Methods, Federal University of Rio de Janeiro, BR

2. Department of Statistics, Federal University of Bahia, BR

3. Center for Mathematics, Computing and Cognition, Federal University of ABC, BR

4. South Africa National Research Foundation Research Chair at the University of Johannesburg, Faculty of Education, ZA

Publisher

Institute of Mathematical Statistics

Subject

Applied Mathematics,Statistics and Probability

Reference58 articles.

1. Kowal, D. R., Matteson, D. S., and Ruppert, D. (2019). “Dynamic shrinkage processes.” Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(4): 781–804.

2. Piironen, J. and Vehtari, A. (2017). “Sparsity information and regularization in the horseshoe and other shrinkage priors.” Electronic Journal of Statistics, 11(2): 5018–5051.

3. Nakajima, J. and West, M. (2013). “Bayesian analysis of latent threshold dynamic models.” Journal of Business & Economic Statistics, 31(2): 151–164.

4. Balardin, J. B., Zimeo Morais, G. A., Furucho, R. A., Trambaiolli, L., Vanzella, P., Biazoli Jr, C., and Sato, J. R. (2017). “Imaging brain function with functional near-infrared spectroscopy in unconstrained environments.” Frontiers in human neuroscience, 11: 258.

5. Balconi, M., Grippa, E., and Vanutelli, M. E. (2015). “What hemodynamic (fNIRS), electrophysiological (EEG) and autonomic integrated measures can tell us about emotional processing.” Brain and cognition, 95: 67–76.

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