Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data
It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct...
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| Published in: | Journal of neuroscience methods Vol. 150; no. 2; pp. 228 - 237 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Netherlands
Elsevier B.V
30-01-2006
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| Abstract | It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method. |
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| AbstractList | It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method. |
| Author | Ding, Mingzhou Bressler, Steven L. Chen, Yonghong |
| Author_xml | – sequence: 1 givenname: Yonghong surname: Chen fullname: Chen, Yonghong email: ychen@bme.ufl.edu organization: Department of Biomedical Engineering, University of Florida, 102B BME Building, Gainesville, FL 32611-6131, USA – sequence: 2 givenname: Steven L. surname: Bressler fullname: Bressler, Steven L. email: bressler@fau.edu organization: Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA – sequence: 3 givenname: Mingzhou surname: Ding fullname: Ding, Mingzhou email: mding@bme.ufl.edu organization: Department of Biomedical Engineering, University of Florida, 102B BME Building, Gainesville, FL 32611-6131, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16099512$$D View this record in MEDLINE/PubMed |
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| Keywords | Frequency domain Multiple time series Conditional Granger causality Multivariate autoregressive (MVAR) model Autoregressive moving average (ARMA) process Partition matrix Granger causality |
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| SubjectTerms | Algorithms Animals Autoregressive moving average (ARMA) process Brain - physiology Conditional Granger causality Evoked Potentials - physiology Frequency domain Granger causality Models, Neurological Multiple time series Multivariate autoregressive (MVAR) model Neurons - physiology Partition matrix |
| Title | Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data |
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