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Estimation of high-dimensional connectivity in FMRI data via subspace autoregressive models

We consider the challenge in estimating effective connectivity of brain networks with a large number of nodes from fMRI data. The classical vector autoregressive (VAR) modeling tends to produce unreliable estimates for large dimensions due to the huge number of parameters. We propose a subspace esti...

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書目詳細資料
Main Authors:	Ting, C. M., Seghouane, A. K., Salleh, S. H.
格式:	Conference or Workshop Item
出版:	IEEE Computer Society 2016
主題:	QH Natural history
在線閱讀:	http://eprints.utm.my/id/eprint/73097/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987892034&doi=10.1109%2fSSP.2016.7551799&partnerID=40&md5=32b51c62976b05b5c2a2865f1f2a45e3
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