Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control

Wanrong Zhang, Yajun Mei

Research output: Contribution to journalArticlepeer-review

Abstract

In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments. In this article, we propose to incorporate multi-armed bandit approaches into sequential change-point detection to develop an efficient bandit change-point detection algorithm based on the limiting Bayesian approach to incorporate a prior knowledge of potential changes. Our proposed algorithm, termed Thompson-Sampling-Shiryaev-Roberts-Pollak (TSSRP), consists of two policies per time step: the adaptive sampling policy applies the Thompson Sampling algorithm to balance between exploration for acquiring long-term knowledge and exploitation for immediate reward gain, and the statistical decision policy fuses the local Shiryaev–Roberts–Pollak statistics to determine whether to raise a global alarm by sum shrinkage techniques. Extensive numerical simulations and case studies demonstrate the statistical and computational efficiency of our proposed TSSRP algorithm.

Original languageEnglish (US)
Pages (from-to)33-43
Number of pages11
JournalTechnometrics
Volume65
Issue number1
DOIs
StatePublished - 2023

Keywords

  • Adaptive sampling
  • Change-point detection
  • Partially observed variables
  • Shiryaev–Roberts procedure
  • Thompson sampling

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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