Wald's Complete Class Theorem states that, for a given statistical problem, all admissible decision rules are essentially derived from the most optimal or minimax decision rule. This means that if a decision rule is admissible, it must be at least as good as the minimax rule in terms of minimizing the maximum risk. The theorem connects the concept of admissibility with the idea of optimality in statistical decision theory, highlighting the relationship between these two concepts in determining effective decision-making procedures.
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