A Bayesian State-Space Approach to Mapping Directional Brain Networks-Reference-Cited by-同舟云学术

A Bayesian State-Space Approach to Mapping Directional Brain Networks

Published:2021-02-10 Issue:536 Volume:116 Page:1637-1647
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Li Huazhang¹,Wang Yaotian²,Yan Guofen³,Sun Yinge¹,Tanabe Seiji⁴,Liu Chang-Chia⁵,Quigg Mark S.⁶,Zhang Tingting²

Affiliation:

1. Department of Statistics, University of Virginia, Charlottesville, VA;

2. Department of Statistics, University of Pittsburgh, Pittsburgh, PA;

3. Department of Public Health Sciences, University of Virginia, Charlottesville, VA;

4. Department of Psychology, University of Virginia, Charlottesville, VA;

5. Department of Neurosurgery, University of Virginia, Charlottesville, VA;

6. Department of Neurology, University of Virginia, Charlottesville, VA

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2020.1865985

Reference61 articles.

1. Arroyo-Relión, J. D., Kessler, D., Levina, E., and Taylor, S. F. (2017), “Network Classification With Applications to Brain Connectomics,” arXiv no. 1701.08140.

2. Partial directed coherence: a new concept in neural structure determination

3. Regularized estimation in sparse high-dimensional time series models

4. Multivariate Bayesian variable selection and prediction

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