Package: onlineCOV 1.3

Jun Li

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>.

Authors:Lingjun Li and Jun Li

onlineCOV_1.3.tar.gz
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onlineCOV_1.3.tgz(r-4.4-emscripten)onlineCOV_1.3.tgz(r-4.3-emscripten)
onlineCOV.pdf |onlineCOV.html✨
onlineCOV/json (API)

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2 exports 0.00 score 0 dependencies 115 downloads

Last updated 4 years agofrom:aa8590e84a. Checks:OK: 2. Indexed: yes.

Exports:nuisance.eststopping.rule

Dependencies: