Abstract
The problem of detection of a change in distribution is considered. Shiryayev (1963, Theory Probab. Appl., 8, pp. 22–46, 247–264 and 402–413; 1978, Optimal Stopping Rules, Springer, New York) solved the problem in a Bayesian framework assuming that the prior on the change point is Geometric (p). Shiryayev showed that the Bayes solution prescribes stopping as soon as the posterior probability of the change having occurred exceeds a fixed level. In this paper, a myopic policy is studied. An empirical Bayes stopping time is investigated for detecting a change in distribution when the prior is not completely known.
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Research was supported in part by the Natural Sciences and Engineering Research Council of Canada under grant GP 7987.
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Karunamuni, R.J., Zhang, S. Empirical Bayes detection of a change in distribution. Ann Inst Stat Math 48, 229–246 (1996). https://doi.org/10.1007/BF00054787
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DOI: https://doi.org/10.1007/BF00054787