onlineCOV: Online Change Point Detection in High-Dimensional Covariance
Structure
Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. Moreover, it allows both spatial and temporal dependence in data. The dependence can be estimated by a data-driven procedure. The level of threshold in the stopping rule can be determined at a pre-selected average run length. More detail can be seen in Li, L. and Li, J. (2020) "Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks." <doi:10.48550/arXiv.1911.07762>.
| Version: |
1.3 |
| Published: |
2020-03-23 |
| Author: |
Lingjun Li and Jun Li |
| Maintainer: |
Jun Li <jli49 at kent.edu> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| CRAN checks: |
onlineCOV results |
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