Multiple test procedures using an upper bound of the number of true hypotheses and their use for evaluating high-dimensional EEG data
Multiple test procedures using an upper bound of the number of true hypotheses and their use for evaluating high-dimensional EEG data
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DOI：
10.1016/j.jneumeth.2007.12.013
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发表时间：
2008-05-15
影响因子：
3
通讯作者：
Vollandt, Ruediger
中科院分区：
文献类型：
--
作者：
Hemmelmann, Claudia;Ziegler, Andreas;Vollandt, Ruediger
脑电图数据的频率分析产生大量的数据集，这些数据集是高维的，必须在统计上进行评估，而不存在大量的假阳性陈述。有几种方法可以在多重比较中处理这个问题。知道真实假设的数量增加了一些多重测试过程的能力，然而真实假设的数量是未知的，一般来说，必须估计。本文利用真假设数目的上界，导出了两个新的多重检验过程。我们的第一个程序控制了广义家庭误差率，因此是对Hommel和Hoffmann的逐步下降程序的改进[Hommel G., Hoffmann T.]控制不确定性。见：Bauer P. Hommel G. Sonnemann E.，编辑。多重假设检验，海德堡：施普林格1987；ISBN 3540505598: p。154 - 61年)。第二个新程序控制了错误发现比例，并改进了Lehmann和Romano的方法[Lehmann e.l., Romano J.P.]。安。Stat。2005;33:1138-54]。通过蒙特卡罗模拟，我们展示了功率增益如何取决于真实假设数量估计的准确性。在一个使用脑电图数据处理记忆词汇项的例子中，我们的程序的增强能力得到了证明。(c) 2008 Elsevier B.V.版权所有
Frequency analyses of EEG data yield large data sets, which are high-dimensional and have to be evaluated statistically without a large number of false positive statements. There exist several methods to deal with this problem in multiple comparisons. Knowing the number of true hypotheses increases the power of some multiple test procedures, however the number of true hypotheses is unknown, in general, and must be estimated. In this paper, we derive two new multiple test procedures by using an upper bound for the number of true hypotheses. Our first procedure controls the generalized family-wise error rate, and thus is an improvement of the step-down procedure of Hommel and Hoffmann [Hommel G., Hoffmann T. Controlled uncertainty. In: Bauer P. Hommel G. Sonnemann E., editors. Multiple Hypotheses Testing, Heidelberg: Springer 1987;ISBN 3540505598:p. 154-61]. The second new procedure controls the false discovery proportion and improves upon the approach of Lehmann and Romano [Lehmann E.L., Romano J.P. Generalizations of the familywise error rate. Ann. Stat. 2005;33:1138-54]. By Monte-Carlo simulations, we show how the gain in power depends upon the accuracy of the estimate of the number of true hypotheses. The gain in power of our procedures is demonstrated in an example using EEG data on the processing of memorized lexical items. (c) 2008 Elsevier B.V. All rights reserved.