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首页> 外文期刊>The American statistician >The Equivalence of Neyman Optimum Allocation for Sampling and Equal Proportions for Apportioning the U.S. House of Representatives
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The Equivalence of Neyman Optimum Allocation for Sampling and Equal Proportions for Apportioning the U.S. House of Representatives

机译:抽样的内曼最佳分配的等价性和分配给美国众议院的均等比例

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摘要

We present a surprising though obvious result that seems to have been unnoticed until now. In particular, we demonstrate the equivalence of two well-known problems-the optimal allocation of the fixed overall sample size n among L strata under stratified random sampling and the optimal allocation of the H = 435 seats among the 50 states for apportionment of the U.S. House of Representatives following each decennial census. In spite of the strong similarity manifest in the statements of the two problems, they have not been linked and they have well-known but different solutions; one solution is not explicitly exact (Neyman allocation), and the other (equal proportions) is exact. We give explicit exact solutions for both and note that the solutions are equivalent. In fact, we conclude by showing that both problems are special cases of a general problem. The result is significant for stratified random sampling in that it explicitly shows how to minimize sampling error var(T_Y) when estimating a total T_Y while keeping the final overall sample size fixed at n; this is usually not the case in practice with Neyman allocation where the resulting final overall sample size might be near n + L after rounding. An example reveals that controlled rounding with Neyman allocation does not always lead to the optimum allocation, that is, an allocation that minimizes variance.
机译:我们提出了一个令人惊讶但显而易见的结果,到目前为止似乎还没有注意到。特别是,我们证明了两个众所周知的问题的等效性:分层随机抽样下L个层中固定总样本量n的最佳分配以及美国50个州中H = 435个席位的最佳分配每次十年一次的人口普查后的众议院。尽管这两个问题在陈述中有很强的相似性,但它们并没有联系在一起,它们具有众所周知的但不同的解决方案。一种解决方案不是明确精确的(Neyman分配),而另一种(相等比例)是精确的。我们为两者提供了明确的精确解决方案,并请注意,这些解决方案是等效的。实际上,我们通过显示两个问题都是普遍问题的特例来得出结论。该结果对于分层随机采样很有意义,因为它明确显示了如何在估计总T_Y时将采样误差var(T_Y)最小化,同时将最终总体样本大小固定为n。 Neyman分配在实践中通常不是这种情况,因为在四舍五入后,最终的最终总体样本大小可能接近n +L。一个示例表明,使用Neyman分配进行的受控舍入并不总是导致最优分配,即,使方差最小的分配。

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