Year: 2025
Type: article
Abstract: We present a distribution-free cumulative sum (CUSUM) procedure designed for online change detection in a time series of low-rank images, particularly when the change causes a mean shift. We represent images as matrix data and allow for temporal dependence, in addition to inherent spatial dependence, before and after the change. The marginal distributions are assumed to be general, not limited to any specific parametric distribution. We propose new monitoring statistics that utilize the low-rank structure of the in-control mean matrix. Additionally, we study the properties of the proposed detection procedure, assessing whether the monitoring statistics effectively capture a mean shift and evaluating the rate of increase in the average run length relative to the control limit in both the in-control and out-of-control cases. The effectiveness of our procedure is demonstrated through simulated and real data experiments.
Source: Sequential Analysis
Authors: Tingnan Gong, Seong-Hee Kim, Yao Xie
Institution: Georgia Institute of Technology
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Awards: ATD: Scanning Dynamic Spatial-Temporal Discrete Events for Threat Detection, Bridging Statistical Hypothesis Tests and Deep Learning for Reliability and Computational Efficiency, CAREER: Quick Detection for Streaming Data Over Dynamic Networks, G2285217686, G3387330441, G4324459455, Data-Driven Optimal Police Patrol Zone Districting and Staffing, Sequential Detection and Prediction for Solar Situation Awareness in Power Networks, G5084972002, AI Institute for Advances in Optimization, G6120277484, G6127310570, G6593845955, G6671297155, G7238632903, G7911581280, G8423965115, G975068223
OpenAlex ID: W4410359436