TU2.R4.2

Syndrome-based Fusion Rules in Heterogeneous Distributed Quickest Change Detection

Wen-Hsuan Li, Yu-Chih Huang, National Yang Ming Chiao Tung University, Taiwan
Session:
Change Point Detection
Track:
11: Information Theory and Statistics
Location:
Omikron II
Presentation Time:
Tue, 9 Jul, 11:50 - 12:10
Session Chair:
Yajun Mei,
Presentation
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Discussion
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Session TU2.R4
TU2.R4.1: High Probability Latency Quickest Change Detection over a Finite Horizon
Yu-Han Huang, Venugopal Veeravalli, University of Illinois at Urbana-Champaign, United States
TU2.R4.2: Syndrome-based Fusion Rules in Heterogeneous Distributed Quickest Change Detection
Wen-Hsuan Li, Yu-Chih Huang, National Yang Ming Chiao Tung University, Taiwan
TU2.R4.3: Quickest Detection in High-Dimensional Linear Regression Models via Implicit Regularization
Qunzhi Xu, Georgia Institute of Technology, United States; Yi Yu, University of Warwick, United Kingdom; Yajun Mei, Georgia Institute of Technology and New York University, United States
TU2.R4.4: Monitoring High-dimensional Streaming Data via Fusing Nonparametric Shiryaev-Roberts Statistics
Xinyuan Zhang, Yajun Mei, Georgia Institute of Technology, United States
Resources
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