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The Power of the Optimal Asymptotic Tests of Composite Statistical Hypotheses

机译:复合统计假设的最优渐近检验的功效

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摘要

The easily computable asymptotic power of the locally asymptotically optimal test of a composite hypothesis, known as the optimal C(α) test, is obtained through a “double” passage to the limit: the number n of observations is indefinitely increased while the conventional measure ξ of the error in the hypothesis tested tends to zero so that ξnn½ → τ ≠ 0. Contrary to this, practical problems require information on power, say β(ξ,n), for a fixed ξ and for a fixed n. The present paper gives the upper and the lower bounds for β(ξ,n). These bounds can be used to estimate the rate of convergence of β(ξ,n) to unity as n → ∞. The results obtained can be extended to test criteria other than those labeled C(α). The study revealed a difference between situations in which the C(α) test criterion is used to test a simple or a composite hypothesis. This difference affects the rate of convergence of the actual probability of type I error to the preassigned level α. In the case of a simple hypothesis, the rate is of the order of n. In the case of a composite hypothesis, the best that it was possible to show is that the rate of convergence cannot be slower than that of the order of n ln n.
机译:复合假设的局部渐近最优检验(称为最优C(α)检验)的易于计算的渐近能力是通过“双”传递到极限而获得的:观察数n无限地增加,而常规测度在检验的假设中,误差的ξ趋于零,从而ξnn 1/2 →τ≠0。与此相反,对于固定的ξ,实际问题需要功率的信息,例如β(ξ,n)。和固定的。本文给出了β(ξ,n)的上下界。这些边界可用于估计β(ξ,n)统一为n→∞的收敛速度。所获得的结果可以扩展到除标记为C(α)的那些标准以外的其他测试标准。该研究揭示了使用C(α)检验标准检验简单或复合假设的情况之间的差异。该差异影响类型I错误的实际概率收敛到预先指定的级别α的速率。在简单假设的情况下,比率约为n 。在复合假设的情况下,可以证明的最好一点是,收敛速度不能慢于n ln n数量级。

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