两比例之差的非迭代置信区间方法

J. Reed
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引用次数: 1

摘要

两个独立比例之差的置信区间的构造是统计推断中最基本的分析之一。有或没有连续性校正的Wald渐近方法的覆盖概率特征小于标称,但尽管这种已知的不良行为,仍继续使用。对于相同样本量的情况,Wald-z覆盖概率总体上是次名义的。Wald-c覆盖概率总是超过标称水平,且区间宽度大于Wald-z。对于不相等样本量的情况,Wald-z总是亚标称水平,而Wald-c则超过标称水平。纽科姆的混合方法和Agresti-Caffo方法对于相同或不相同样本量的覆盖概率接近名义。考虑到覆盖概率标准,纽科姆混合方法和Agresti-Caffo方法表现出更优的覆盖性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non Iterative Confidence Interval Methods for the Difference between Two Proportions
The construction of a confidence interval for the difference between two independent proportions is one of the most basic analyses in statistical inference. The Wald asymptotic methods, with and without a continuity correction have less than nominal coverage probability characteristics but continue to be used in spite of this known poor behavior. For the equal sample size case, the Wald-z coverage probability is subnominal overall. The Wald-c coverage probability always exceeds the nominal level and has interval width larger than Wald-z. For the unequal sample size case, Wald-z is always subnominal while Wald-c exceeds the nominal level. Newcombe's hybrid method and the Agresti-Caffo methods have coverage probabilities that are near nominal for either the equal or unequal sample sizes. Considering the coverage probability criterion, Newcombe's hybrid method and Agresti-Caffo method demonstrate superior coverage properties.
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