High-dimensional Quickest Change Detection with Adaptive Window-Based Subset Estimation
Abstract Number:
2210
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Arghya Chakraborty (1), Georgios Fellouris (2)
Institutions:
(1) N/A, N/A, (2) University of Illinois, Urbana-Champaign, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
A large scale multichannel sequential detection is considered, where an event occurs at some unknown time and affects the distributions of an unknown subset of independent data streams, possibly at a different time each of them. The goal is to detect this change as quickly as possible for any possible affected
set of streams, while controlling the false alarm rate. A computationally scalable adaptive CuSum procedure is proposed. Its performance is analyzed in various high-dimensional regimes where the number of streams goes to infinity as the false alarm rate goes to zero. Moreover, it is compared favorably, both analytically and numerically, to that of existing schemes in the literature with similar computational complexity. Finally, it is shown to enjoy a log-asymptotic optimality property in very sparse or very high dimensional domains.
Keywords:
High-dimensional|Sequential change detection|Adaptive|CuSum|Multistream|Window-based
Sponsors:
Quality and Productivity Section
Tracks:
Miscellaneous
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