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 #320635
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Title:
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Nonparametric Online Change-Point Detection in High Dimensions
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Author(s):
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Ali Shojaie*
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Companies:
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University of Washington
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Keywords:
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Abstract:
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Online change-point detection arises naturally in analysis of sequentially observed data. Recently proposed methods for high-dimensional online change-point detection primarily focus on detecting changes in means or covariance matrices and do not account for more general changes in the data distribution. To bridge this gap, we propose a nonparametric online change-point detection methods for high-dimensional problems based on the extension of energy statistic. We investigate asymptotic properties of the proposed estimator and demonstrate the method using simulated and real data examples.
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Authors who are presenting talks have a * after their name.
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