TY - GEN
T1 - Least favorable distributions for robust quickest change detection
AU - Unnikrishnan, Jayakrishnan
AU - Veeravalli, Venugopal V.
AU - Meyn, Sean
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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U2 - 10.1109/ISIT.2009.5205661
DO - 10.1109/ISIT.2009.5205661
M3 - Conference contribution
AN - SCOPUS:70449470803
SN - 9781424443130
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 649
EP - 653
BT - 2009 IEEE International Symposium on Information Theory, ISIT 2009
T2 - 2009 IEEE International Symposium on Information Theory, ISIT 2009
Y2 - 28 June 2009 through 3 July 2009
ER -