Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

Wen, Xiaotong and Rangarajan, Govindan and Ding, Mingzhou (2011) Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix. In: PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 371 (1997, ).

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Abstract

Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix.

Item Type:	Journal Article
Publication:	PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
Publisher:	ROYAL SOC
Additional Information:	Copyright for this article belongs to the ROYAL SOC, 6-9 CARLTON HOUSE TERRACE, ENGLAND.
Keywords:	multivariate; Granger causality; factorization; spectral density matrix
Department/Centre:	Division of Biological Sciences > Centre for Neuroscience Division of Physical & Mathematical Sciences > Mathematics
Date Deposited:	10 Mar 2014 06:35
Last Modified:	10 Mar 2014 06:35
URI:	http://eprints.iisc.ac.in/id/eprint/48520

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