Abstract
In this paper we introduce a method for change detection which uses randomised subsamples of the data. The method is based on the LSCR (Leave-out Sign-dominant Correlation Regions) algorithm for finite sample system identification which generates a region in parameter space which has a guaranteed probability of containing the true parameter. The change detection problem is formulated as a hypothesis testing problem, and the null-hypothesis is accepted if the parameter representing the hypothesis belongs to the confidence set constructed by the LSCR algorithm. This approach delivers a test with a guaranteed low probability of a false alarm for any finite number of observed data points. The test and the associated theory can be applied under very general conditions reducing the amount of necessary prior information to a minimum. The approach is illustrated on two common change detection problems.
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