Consistent and powerful graph-based change-point test for high-dimensional data.
TL;DR: A distribution-free, consistent graph-based change-point detection for high-dimensional data using a Bayesian-type statistic based on the shortest Hamiltonian path is proposed and proven to be consistent.
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Abstract: 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.
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Citations
SHM deformation monitoring for high-speed rail track slabs and Bayesian change point detection for the measurements
TL;DR: Through posterior distribution information of the Bayesian CPD method, change points which have the highest posterior probability can be identified for the versine deformation of HSR track slabs, which is essential for investigating the cause of the change point and providing maintenance plan in time.
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•Posted Content
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- 01 Jul 2017
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