Since the middle of the twentieth century, the problem of making inferences about the point in a surveyed series of observations at which the underlying distribution changes has been extensively addressed in the economics, biostatistics and statistics literature. Cumulative sum-type statistics have commonly been thought to play a central role in non-sequential change point detections. Alternatively, we present and examine an approach based on the Shiryayev-Roberts scheme. We show that retrospective change point detection policies based on Shiryayev-Roberts statistics are non-asymptotically optimal in the context of most powerful testing. Copyright (c) 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
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Article provided by Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association and Swedish Statistical Association in its journal Scandinavian Journal of Statistics.