确定J个独立二项分布中哪一个成功概率最大

Q3 Mathematics
R. Wilcox
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引用次数: 1

摘要

设p1,…,pJ表示具有二项分布的J个独立随机变量成功的概率,设p(1)≤…≤p(J)按升序表示这些概率。目标是决定哪一组成功的概率最大,p(J)。设p∈1,…,p∈J分别表示p1,…,pJ的估计。该策略是测试J−1个假设,将剩余J−1个组中估计值最大的组进行比较。对于这J−1个被拒绝的假设中的每一个,决定最大估计值对应的组具有更大的成功概率。与简单地执行所有两两比较相比,这种方法具有强大的优势。然而,对于当前的情况,用于控制再出现一个类型I错误的概率的更明显的方法表现得很差。本文描述并说明了一种处理这种情况的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Which of J Independent Binomial Distributions Has the Largest Probability of Success
Let p1,…, pJ denote the probability of a success for J independent random variables having a binomial distribution and let p(1) ≤ … ≤ p(J) denote these probabilities written in ascending order. The goal is to make a decision about which group has the largest probability of a success, p(J). Let p̂1,…, p̂J denote estimates of p1,…,pJ, respectively. The strategy is to test J − 1 hypotheses comparing the group with the largest estimate to each of the J − 1 remaining groups. For each of these J − 1 hypotheses that are rejected, decide that the group corresponding to the largest estimate has the larger probability of success. This approach has a power advantage over simply performing all pairwise comparisons. However, the more obvious methods for controlling the probability of one more Type I errors perform poorly for the situation at hand. A method for dealing with this is described and illustrated.
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来源期刊
CiteScore
0.50
自引率
0.00%
发文量
5
期刊介绍: The Journal of Modern Applied Statistical Methods is an independent, peer-reviewed, open access journal designed to provide an outlet for the scholarly works of applied nonparametric or parametric statisticians, data analysts, researchers, classical or modern psychometricians, and quantitative or qualitative methodologists/evaluators.
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