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  "Title": "A Collection of Change-Point Detection Methods",
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  "Authors@R": "c(\nperson(\"Haotian\", \"Xu\", email=\"haotian.xu@uclouvain.be\", role=c(\"aut\",\"cre\")),\nperson(\"Oscar\", \"Padilla\", email=\"oscar.madrid@stat.ucla.edu\", role=\"aut\"),\nperson(\"Daren\", \"Wang\", email=\"dwang24@nd.edu\", role=\"aut\"),\nperson(\"Mengchu\", \"Li\", email=\"Mengchu.Li@warwick.ac.uk\", role=\"aut\"),\nperson(\"Qin\", \"Wen\", role=\"ctb\")\n)",
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  "Description": "Performs a series of offline and/or online change-point\ndetection algorithms for 1) univariate mean:\n<doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate\npolynomials: <doi:10.1214/21-EJS1963>; 3) univariate and\nmultivariate nonparametric settings: <doi:10.1214/21-EJS1809>,\n<doi:10.1109/TIT.2021.3130330>; 4) high-dimensional\ncovariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional\nnetworks with and without missing values:\n<doi:10.1214/20-AOS1953>, <arXiv:2101.05477>,\n<arXiv:2110.06450>; 6) high-dimensional linear regression\nmodels: <arXiv:2010.10410>, <arXiv:2207.12453>; 7)\nhigh-dimensional vector autoregressive models:\n<arXiv:1909.06359>; 8) high-dimensional self exciting point\nprocesses: <arXiv:2006.03572>; 9) dependent dynamic\nnonparametric random dot product graphs: <arXiv:1911.07494>;\n10) univariate mean against adversarial attacks:\n<arXiv:2105.10417>.",
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  "Date/Publication": "2023-10-07 11:28:54 UTC",
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