Name
Title | ||
|---|---|---|
Autoregressive causal relation: Digital filtering approach to causality measures in frequency domain. |
Abstract | ||
|---|---|---|
A novel measure of the Autoregressive Causal Relation based on a multivariate autoregressive model is proposed. It reveals the strength of the connections among a simultaneous time series and also the direction of the information flow. It is defined in the frequency domain, similar to the formerly published methods such as: Directed Transfer Function, Direct Directed Transfer Function, Partial Directed Coherence, and Generalized Partial Directed Coherence. Compared to the Granger causality concept, frequency decomposition extends the possibility to reveal the frequency rhythms participating on the information flow in causal relations. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.dsp.2013.04.006 | Digital Signal Processing |
Keywords | Field | DocType |
Autoregressive processes,Frequency domain analysis,Brain modeling,Electroencephalography | Econometrics,Causality,Digital filter,Granger causality,Artificial intelligence,Causal system,Frequency domain,Autoregressive model,Pattern recognition,Algorithm,Coherence (physics),Transfer function,Mathematics | Journal |
Volume | Issue | ISSN |
23 | 5 | 1051-2004 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomás Boril | 1 | 4 | 2.51 |
| Pavel Sovka | 2 | 63 | 14.08 |