文章基本信息

标题：Consistent and powerful graph-based change-point test for high-dimensional data
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作者：Xiaoping Shi ; Yuehua Wu ; Calyampudi Radhakrishna Rao 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2017
卷号：114
期号：15
页码：3873-3878
DOI：10.1073/pnas.1702654114
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：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.
关键词：Bayesian-type statistic ; shortest Hamilton path ; ratio cut ; minimum spanning tree ; cell division