Selection Procedures for A Problem in Analysis of Variance.
PURDUE UNIV LAFAYETTE IN DEPT OF STATISTICS
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There are many situations in the analysis of variance where an experimenter would like to make comparisons among and select the best set the treatments. In this paper we study the problem where the data are based on a completely randomized block design. It is shown that the subset selection approach is a useful method to make appropriate identification among the hypotheses and the selected subset. We propose an optimal selection procedure which controls the error probabilities when all the parameters treatments are equal and which maximize the infimum of the probability of a correct selection over some preference parameter space, simultaneously. Some examples are provided to illustrate the optimal subset selection rule and its interpretation in terms of the identified hypotheses. Author
- Statistics and Probability