A Statistical Selection Approach to Binomial Models

Abstract

A subset selection rule is considered for selecting the best of k binomial populations (as determined by the binomial probability parameter). Let Xi denote the number of conforming items in a sample of size n from the ith population (success probability pi), i = 1, …, k. The rule selects the ith population if and only if , where d is a nonnegative integer. Operating characteristics are studied for slippage and equi-spaced parametric configurations. Tables and graphs relating to selection probabilities and expected subset size are presented as well as examples for illustrating their use. Also, a new rule is discussed for selecting populations when bounds on the probability parameters are available.

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