A Lower Confidence Bound on the Probability of a Correct Selection
在从多个总体中选出最优的问题中，推导了正确选择概率的保守下置信界，适用于具有单调似然比性质的分布，并给出了正态总体的应用表格。
Abstract In the problem of selecting the best of k populations, a natural rule is to select the population corresponding to the largest sample value of an appropriate statistic. As a retrospective analysis, a conservative lower confidence bound on the probability of a correct selection is derived when the probability density function has the monotone likelihood ratio property under the location parameter setting. The result is applied to the normal populations with both known and unknown common variance. Tables to implement the confidence bound are provided.