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Consistent and powerful graph-based change-point test for high-dimensional data
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DOI：10.1073/pnas.1702654114.png)
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摘要
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A change-point detection is proposed by using a Bayesian-type statistic based on the shortest Hamiltonian path, and the change-point is estimated by using ratio cut. A permutation procedure is applied to approximate the significance of Bayesian-type statistics. The change-point test is proven to be consistent, and an error probability in change-point estimation is provided. The test is very powerful against alternatives with a shift in variance and is accurate in change-point estimation, as shown in simulation studies. Its applicability in tracking cell division is illustrated.
Keyword:
Bayesian-type statistic
shortest Hamilton path
ratio cut
minimum spanning tree
cell division
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9.1
论文数:
10.8W
被引数:
73.5W
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Multiple-change-point detection for high dimensional time series via sparsified binary segmentation基于稀疏二进制分割的高维时间序列多变化点检测OAAI
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