Abstract
The problem of selecting the best among k (≥ 2) uniform distributions is studied via the empirical Bayes approach. A Bayes selection procedure d* with respect to an estimated prior distribution is proposed for empirical Bayes selection procedure. We show the consistency of the estimated prior distribution and establish the asymptotic optimality of the procedure d*. It is found that under certain conditions, the convergence rate is of order 0((lnn/n)1/4) where n is the number of accumulated past observations at hand.
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Huang, WT., Liang, T. (1997). Empirical Bayes Rules for Selecting the Best Uniform Populations. In: Panchapakesan, S., Balakrishnan, N. (eds) Advances in Statistical Decision Theory and Applications. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-1-4612-2308-5_6
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DOI: https://doi.org/10.1007/978-1-4612-2308-5_6
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