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Granger causality with signal-dependent noise
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Luo, Qiang, Ge, Tian and Feng, Jianfeng (2011) Granger causality with signal-dependent noise. Neuroimage, Vol.57 (No.4). pp. 1422-1429. doi:10.1016/j.neuroimage.2011.05.054
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Official URL: http://dx.doi.org/10.1016/j.neuroimage.2011.05.054
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 |
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| 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 | ||||
| Official Date: | 15 August 2011 | ||||
| Dates: |
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| Volume: | Vol.57 | ||||
| Number: | No.4 | ||||
| Page Range: | pp. 1422-1429 | ||||
| DOI: | 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) |
Data sourced from Thomson Reuters' Web of Knowledge
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