The Lehmann-Scheffé Theorem states that if you have a complete sufficient statistic for a parameter, any unbiased estimator that is a function of that statistic is the best unbiased estimator, meaning it has the smallest variance among all unbiased estimators. This theorem highlights the importance of complete sufficient statistics in the context of statistical inference and provides a foundation for developing optimal estimators, particularly in relation to exponential families and decision theory.
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