TY - GEN
T1 - Asymptotic optimality of D-CuSum for quickest change detection under transient dynamics
AU - Zou, Shaofeng
AU - Fellouris, Georgios
AU - Veeravalli, Venugopal V.
N1 - ACKNOWLEDGMENT The work of Shaofeng Zou and Venugopal V. Veeravalli was supported by the National Science Foundation (NSF) under grants CIF 1514245 and ECCS 146231. The work of Georgios Fellouris was supported by the NSF grant CIF 1514245.
PY - 2017/8/9
Y1 - 2017/8/9
N2 - The problem of quickest change detection (QCD) under transient dynamics is studied, in which the change from the initial distribution to the final persistent distribution does not happen instantaneously, but after a series of cascading transient phases. It is assumed that the durations of the transient phases are deterministic but unknown. The goal is to detect the change as quickly as possible subject to a constraint on the average run length to false alarm. The dynamic CuSum (D-CuSum) algorithm is investigated, which is based on reformulating the QCD problem into a dynamic composite hypothesis testing problem, and has a recursion that facilitates implementation. We show that this algorithm is adaptive to the unknown change point, as well as the unknown transient duration. And under mild conditions of the pre-change and post-change distributions, its asymptotic optimality is demonstrated for all possible asymptotic regimes as the transient duration and the average run length to false alarm go to infinity.
AB - The problem of quickest change detection (QCD) under transient dynamics is studied, in which the change from the initial distribution to the final persistent distribution does not happen instantaneously, but after a series of cascading transient phases. It is assumed that the durations of the transient phases are deterministic but unknown. The goal is to detect the change as quickly as possible subject to a constraint on the average run length to false alarm. The dynamic CuSum (D-CuSum) algorithm is investigated, which is based on reformulating the QCD problem into a dynamic composite hypothesis testing problem, and has a recursion that facilitates implementation. We show that this algorithm is adaptive to the unknown change point, as well as the unknown transient duration. And under mild conditions of the pre-change and post-change distributions, its asymptotic optimality is demonstrated for all possible asymptotic regimes as the transient duration and the average run length to false alarm go to infinity.
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U2 - 10.1109/ISIT.2017.8006932
DO - 10.1109/ISIT.2017.8006932
M3 - Conference contribution
AN - SCOPUS:85034020515
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 2263
EP - 2267
BT - 2017 IEEE International Symposium on Information Theory, ISIT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Symposium on Information Theory, ISIT 2017
Y2 - 25 June 2017 through 30 June 2017
ER -