Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes

2020/06/05 by Daren Wang, Yi Yu, Wang, Daren +3 · 1 citation
Business, Management and Accounting · Computer Science · #Age of Information Optimization #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistics Theory (math.ST) #Supply Chain and Inventory Management

paper · pdf · doi:10.48550/arxiv.2006.03572

openalex publication_date 2020/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 paper, 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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