Asymptotic minimax robust quickest change detection for dependent stochastic processes with parametric uncertainty

Molloy, Timothy L. & Ford, Jason J. (2016) Asymptotic minimax robust quickest change detection for dependent stochastic processes with parametric uncertainty. IEEE Transactions on Information Theory, 62(11), pp. 6594-6608.

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In this paper, we consider the problem of quickly detecting an unknown change in the conditional den- sities of a dependent stochastic process. In contrast to the existing quickest change detection approaches for dependent stochastic processes, we propose minimax robust versions of the popular Lorden, Pollak, and Bayesian criteria for when there is uncertainty about the parameter of the post-change conditional densities. Under an information-theoretic Pythagorean inequality condition on the uncertainty set of possible post-change parameters, we identify asymptotic minimax robust solutions to our Lorden, Pollak, and Bayesian problems. Finally, through simulation examples, we illustrate that asymptotically minimax robust rules can provide detection performance comparable to the popular (but more computationally expensive) generalised likelihood ratio rule.

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ID Code: 98871
Item Type: Journal Article
Refereed: Yes
Keywords: Quickest change detection, minimax robustness, Shiryaev test, CUSUM test
DOI: 10.1109/TIT.2016.2606425
ISSN: 0018-9448
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Probability Theory (010404)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 IEEE
Copyright Statement: Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information
Deposited On: 15 Sep 2016 23:09
Last Modified: 27 Oct 2016 05:29

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