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Activity Number:
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7
- Point Process Modeling and Estimation on Modern Recurrent Event Data
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Type:
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Invited
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Date/Time:
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Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #316931
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Title:
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Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes
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Author(s):
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Rebecca Willett and Daren Wang and Yi Yu*
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Companies:
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University of Chicago and University of Notre Dame and University of Warwick
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Keywords:
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change detection;
point processes
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Abstract:
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High-dimensional self-exciting point processes have been widely used in many application areas to model discrete event data in which past and current events affect the likelihood of future events. In this work, we are concerned with detecting abrupt changes of the coefficient matrices in discrete-time high-dimensional self-exciting Poisson processes, which have yet to be studied in the existing literature due to both theoretical and computational challenges rooted in the non-stationary and high-dimensional nature of the underlying process. We propose a penalized dynamic programming approach which is supported by a theoretical rate analysis and numerical evidence.
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Authors who are presenting talks have a * after their name.
