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Activity Number:
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280
- Leading the Stream: Novel Methods for Streaming Data
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #325462
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Title:
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Sequential Change-Point Detection Based on Nearest Neighbors
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Author(s):
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Hao Chen*
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Companies:
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University of California, Davis
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Keywords:
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graph-based tests;
high-dimensional data;
non-Euclidean data;
tail probability;
average run length;
scan statistic
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Abstract:
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As we observe the dynamics of social networks over time, how can we tell if a significant change happens? We propose a new framework for the detection of change-points as data are generated. The approach utilizes nearest neighbor information and can be applied to ongoing sequences of multivariate data or object data. Different stopping times are compared and one relies on recent observations is recommended. An accurate analytic approximation is obtained for the average run length when there is no change, facilitating its application to real problems.
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Authors who are presenting talks have a * after their name.
