The problem of quickest change detection is studied, where there is an additional constraint on the cost of observations used before the change point and where the post-change distribution is composite. Minimax formulations are proposed for this problem. It is assumed that the post-change family of distributions has a member which is least favorable in a well-defined sense. An algorithm is proposed in which ON-OFF observation control is employed using the least favorable distribution, and a generalized likelihood ratio-based approach is used for change detection. Under additional conditions on the post-change family of distributions, it is shown that the proposed algorithm is asymptotically optimal, uniformly for all possible post-change distributions. Index Terms-Asymptotic optimality, CuSum, exponential family, generalized likelihood ratio, least favourable distribution, minimax, observation control, quickest change detection, unknown post-change distribution. 0018-9448
In REF , the authors developed a dataefficient scheme that allows for optional sampling of the observations in the case when either the post-change family of distributions is finite, or both the pre-and post-change distributions belong to a one parameter exponential family.
Data-Efficient Minimax Quickest Change Detection With Composite Post-Change Distribution
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Mathematics Computer Science