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  "Title": "Online Change Point Detection in High-Dimensional Covariance\nStructure",
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  "Author": "Lingjun Li and Jun Li",
  "Maintainer": "Jun Li <jli49@kent.edu>",
  "Description": "Implement a new stopping rule to detect anomaly in the\ncovariance structure of high-dimensional online data. The\ndetection procedure can be applied to Gaussian or non-Gaussian\ndata with a large number of components. Moreover, it allows\nboth spatial and temporal dependence in data. The dependence\ncan be estimated by a data-driven procedure. The level of\nthreshold in the stopping rule can be determined at a\npre-selected average run length. More detail can be seen in Li,\nL. and Li, J. (2020) \"Online Change-Point Detection in\nHigh-Dimensional Covariance Structure with Application to\nDynamic Networks.\" <arXiv:1911.07762>.",
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