TY - GEN
T1 - Joint Sequential Detection and Isolation of Anomalies under Composite Hypotheses
AU - Chaudhuri, Anamitra
AU - Fellouris, Georgios
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - A setup with multiple, independent, sequentially monitored data streams is considered. For each of them, two composite hypotheses are postulated, with the interpretation that the stream is anomalous if the corresponding alternative hypothesis holds. It is of interest to detect as quickly as possible whether there is at least one anomalous stream, and also to identify upon stopping the subset of anomalous streams. To address this joint sequential detection and isolation problem, we propose a sequential multiple testing framework where the probabilities of four kinds of error are controlled below distinct, user-specified levels. Two of them refer to the detection task, and the other two to the isolation task. A testing policy is proposed and it is shown to achieve the minimum possible expected sample size, under each point of the parameter space, to a first order asymptotic approximation as the four target error probabilities go to 0. The general theory is illustrated in the case that the data streams generate iid observations that belong to a multiparameter exponential family.
AB - A setup with multiple, independent, sequentially monitored data streams is considered. For each of them, two composite hypotheses are postulated, with the interpretation that the stream is anomalous if the corresponding alternative hypothesis holds. It is of interest to detect as quickly as possible whether there is at least one anomalous stream, and also to identify upon stopping the subset of anomalous streams. To address this joint sequential detection and isolation problem, we propose a sequential multiple testing framework where the probabilities of four kinds of error are controlled below distinct, user-specified levels. Two of them refer to the detection task, and the other two to the isolation task. A testing policy is proposed and it is shown to achieve the minimum possible expected sample size, under each point of the parameter space, to a first order asymptotic approximation as the four target error probabilities go to 0. The general theory is illustrated in the case that the data streams generate iid observations that belong to a multiparameter exponential family.
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U2 - 10.1109/ISIT57864.2024.10619649
DO - 10.1109/ISIT57864.2024.10619649
M3 - Conference contribution
AN - SCOPUS:85202796662
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1486
EP - 1491
BT - 2024 IEEE International Symposium on Information Theory, ISIT 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Symposium on Information Theory, ISIT 2024
Y2 - 7 July 2024 through 12 July 2024
ER -