Name
Abstract | ||
|---|---|---|
Detection of a causal relationship between two or more sets of data is an important problem across various scientific disciplines. The Granger causality index and its derivatives are important metrics developed and used for this purpose. However, the test statistics based on these metrics ignore the effect of practical measurement impairments such as subsampling, additive noise, and finite sample ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSP.2018.2872004 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Time series analysis,Detectors,Additive noise,Predictive models,Indexes,Noise measurement | Time series,Monte Carlo method,Mathematical optimization,Causality,Test statistic,Noise measurement,Granger causality,Constant false alarm rate,Statistics,Statistical hypothesis testing,Mathematics | Journal |
Volume | Issue | ISSN |
66 | 22 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ribhu | 1 | 23 | 5.62 |
| Chandra R. Murthy | 2 | 542 | 75.73 |
| Govindan Rangarajan | 3 | 111 | 11.23 |