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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." <arXiv:1911.07762>.
Getting started
Browse package contents
Package details |
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| Author | Lingjun Li and Jun Li |
| Maintainer | Jun Li <jli49@kent.edu> |
| License | GPL (>= 2) |
| Version | 1.3 |
| Package repository | View on CRAN |
| Installation |
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