Abstract
The problem of selecting the population with the largest probability of success from among k(≥ 2) independent Bernoulli populations is investigated. The population to be selected must be as good as or better than a control. It is assumed that past observations are available when the current selection is made. Therefore, the empirical Bayes approach is employed. Combining useful information from the past data, an empirical. Bayes two-stage selection procedure is developed. It is proved that the proposed empirical Bayes two-stage selection procedure is asymptotically optimal, having a rate of convergence of order O(exp(-cn)), for some positive constant c, where n is the number of past observations at hand.
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