期刊
ANNALS OF STATISTICS
卷 47, 期 3, 页码 1381-1407出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/18-AOS1718
关键词
Change-point; sequential detection; graph-based tests; nonparametrics; scan statistic; tail probability; high-dimensional data; network data; non-Euclidean data
资金
- NSF [DMS-1513653]
We propose a new framework for the detection of change-points in online, sequential data analysis. The approach utilizes nearest neighbor information and can be applied to sequences of multivariate observations or non-Euclidean data objects, such as network data. Different stopping rules are explored, and one specific rule is recommended due to its desirable properties. An accurate analytic approximation of the average run length is derived for the recommended rule, making it an easy off-the-shelf approach for real multivariate/object sequential data monitoring applications. Simulations reveal that the new approach has better performance than likelihood-based approaches for high dimensional data. The new approach is illustrated through a real dataset in detecting global structural changes in social networks.
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