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Detecting abrupt changes in high-dimensional self-exciting Poisson processes
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Wang, Daren, Yu, Yi and Willett, Rebecca (2023) Detecting abrupt changes in high-dimensional self-exciting Poisson processes. Statistica Sinica, 33 . pp. 1653-1671. doi:10.5705/ss.202021.0221 ISSN 1017-0405.
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Official URL: https://doi.org/10.5705/ss.202021.0221
Abstract
High-dimensional self-exciting point processes are widely used to model discrete event data in which past and current events affect the likelihood of future events. In this study, we detect abrupt changes in the coefficient matrices of discrete-time high-dimensional self-exciting Poisson processes, which have yet to be studied because of the theoretical and computational challenges in the nonstationary and high-dimensional nature of the underlying process. We propose a penalized dynamic programming approach, supported by a theoretical rate analysis and numerical evidence.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Library of Congress Subject Headings (LCSH): | Poisson processes | ||||||
Journal or Publication Title: | Statistica Sinica | ||||||
Publisher: | Institute of Statistical Science, Academia Sinica | ||||||
ISSN: | 1017-0405 | ||||||
Official Date: | 8 May 2023 | ||||||
Dates: |
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Volume: | 33 | ||||||
Page Range: | pp. 1653-1671 | ||||||
DOI: | 10.5705/ss.202021.0221 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Description: | Special issue |
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Date of first compliant deposit: | 23 November 2023 | ||||||
Date of first compliant Open Access: | 29 November 2023 | ||||||
RIOXX Funder/Project Grant: |
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