Estimating the false discovery rate using the stochastic approximation algorithm
Estimating the false discovery rate using the stochastic approximation algorithm
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DOI：
10.1093/biomet/asn036
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发表时间：
2008-12-01
期刊：
影响因子：
2.7
通讯作者：
Zhang, Jian
中科院分区：
文献类型：
--
作者：
Liang, Faming;Zhang, Jian
多个假设的检验涉及到在某些应用中强烈依赖的统计数据，但这一主题的大多数工作都是基于独立性假设。本文提出了一种估计多假设检验的错误发现率的新方法，该方法采用随机逼近算法通过最小化未知密度与其估计量之间的Kullback-Leibler距离来参数估计考试分数的密度，并采用集合平均方法估计错误发现率。我们的方法适用于检验统计量之间的一般相关性。在模拟和真实数据示例上，将本文方法与几种竞争方法进行了数值比较，结果表明本文方法在几乎所有场景下都能更准确地控制错误发现率。
Testing of multiple hypotheses involves statistics that are strongly dependent in some applications, but most work on this subject is based on the assumption of independence. We propose a new method for estimating the false discovery rate of multiple hypothesis tests, in which the density of test scores is estimated parametrically by minimizing the Kullback-Leibler distance between the unknown density and its estimator using the stochastic approximation algorithm, and the false discovery rate is estimated using the ensemble averaging method. Our method is applicable under general dependence between test statistics. Numerical comparisons between our method and several competitors, conducted on simulated and real data examples, show that our method achieves more accurate control of the false discovery rate in almost all scenarios.