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Activity Number:
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118
- Recent Advances in Change-Point Analysis
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Type:
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Invited
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Date/Time:
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Monday, August 8, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #319255
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Title:
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Graph-Based Multiple Change-Point Detection
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Author(s):
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Yuxuan Zhang and Hao Chen*
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Companies:
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University of California, Davis and University of California, Davis
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Keywords:
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High-dimensional data;
Network data;
various change types;
wild binary segmentation;
seeded binary segmentation;
hierarchical structure
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Abstract:
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We propose a new multiple change-point detection framework for multivariate and non-Euclidean data. First, we combine graph-based statistics with wild binary segmentation or seeded binary segmentation to search for a pool of candidate change-points. We then prune the candidate change-points through a novel goodness-of-fit statistic. Numerical studies show that this new framework outperforms existing methods under a wide range of settings. The resulting change-points can further be arranged hierarchically based on the goodness-of-fit statistic. The new framework is illustrated on a Neuropixels recording of an awake mouse.
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Authors who are presenting talks have a * after their name.
