Measuring frequency domain Granger causality for multiple blocks of interacting time series
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Publication:353890
DOI10.1007/s00422-013-0547-5zbMath1267.92022OpenAlexW2094195939WikidataQ44709209 ScholiaQ44709209MaRDI QIDQ353890
Publication date: 16 July 2013
Published in: Biological Cybernetics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00422-013-0547-5
block-based connectivity analysisdirected coherenceneurophysiological time seriespartial directed coherencevector autoregressive (VAR) models
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Neural biology (92C20) Computational methods for problems pertaining to biology (92-08)
Uses Software
Cites Work
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- Granger causality and path diagrams for multivariate time series
- Measuring connectivity in linear multivariate processes: definitions, interpretation, and practical analysis
- Block coherence: a method for measuring the interdependence between two blocks of neurobiological time series
- A new method of the description of the information flow in the brain structures
- Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems
- Granger-causality in multiple time series
- Causation, prediction, and search
- Extended causal modeling to assess partial directed coherence in multiple time series with significant instantaneous interactions
- Information theoretic interpretation of frequency domain connectivity measures
- Partial directed coherence: a new concept in neural structure determination
- Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance
- On the evaluation of information flow in multivariate systems by the directed transfer function
- Quantitative analysis of directional strengths in jointly stationary linear multivariate processes
- Measures of Conditional Linear Dependence and Feedback Between Time Series
- Measurement of Linear Dependence and Feedback Between Multiple Time Series
- Testing Causality Between Two Vectors in Multivariate Autoregressive Moving Average Models
- Vector Autoregressions and Causality
- Representations of jointly stationary stochastic feedback processes†
- Investigating Causal Relations by Econometric Models and Cross-spectral Methods
