Luo, Qiang, Ge, Tian and Feng, Jianfeng. (2011) Granger causality with signal-dependent noise. Neuroimage, Vol.57 (No.4). pp. 1422-1429. ISSN 1053-8119

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Abstract

It is generally believed that the noise variance in in vivo neuronal data exhibits time-varying volatility, particularly signal-dependent noise. Despite a widely used and powerful tool to detect causal influences in various data sources, Granger causality has not been well tailored for time-varying volatility models. In this technical note, a unified treatment of the causal influences in both mean and variance is naturally proposed on models with signal-dependent noise in both time and frequency domains. The approach is first systematically validated on toy models, and then applied to the physiological data collected from Parkinson patients, where a clear advantage over the classical Granger causality is demonstrated.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Time-series analysis, Statistical hypothesis testing, Signal processing, Parkinson's disease, Brain -- Imaging -- Statistical methods
Journal or Publication Title:	Neuroimage
Publisher:	Elsevier
ISSN:	1053-8119
Date:	15 August 2011
Volume:	Vol.57
Number:	No.4
Page Range:	pp. 1422-1429
Identification Number:	10.1016/j.neuroimage.2011.05.054
Status:	Peer Reviewed
Publication Status:	Published
Funder:	Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC)
Grant number:	60904065 (NSFC), 71031007 (NSFC)
URI:	http://wrap.warwick.ac.uk/id/eprint/38531

Data sourced from Thomson Reuters' Web of Knowledge

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