Abstract
We study the problem of robust quickest change detection where the pre-change and post-change distributions are not known exactly but belong to known uncertainty classes of distributions. Both Bayesian and minimax versions of the quickest change detection problem are considered. When the uncertainty classes satisfy some specific conditions, we identify least favorable distributions (LFD's) from the uncertainty classes, and show that the detection rule designed for the LFD's is optimal in a minimax sense. The condition is similar to that required for the existence of LFD's for the robust hypothesis testing problem studied by Huber.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
