A Factor Model Approach to Multiple Testing Under Dependence
A Factor Model Approach to Multiple Testing Under Dependence
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DOI：
10.1198/jasa.2009.tm08332
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发表时间：
2009-12-01
影响因子：
3.7
通讯作者：
Causeur, David
中科院分区：
文献类型：
--
作者：
Friguet, Chloe;Kloareg, Maela;Causeur, David
个体检验统计量之间的相关性是目前高维数据多重检验文献中讨论最多的主题之一，特别是自Benjamini和Hochberg（1995）引入错误发现率（FDR）以来。许多论文首先关注的是依赖性对FDR控制的影响。索尼克最近的作品已经调查的方法，占共同的信息共享的所有变量，以稳定的错误率的分布。同样，我们建议通过因子分析结构来模拟这种信息共享，用于检验统计量的条件方差。结果表明，错误发现数的方差沿着共同方差的增加而增加。本文推导了一般线性对比的检验统计量，利用公因子结构减小了误差率的方差，提出了条件FDR估计，并证明了多重检验过程的总体性能相对于经典过程在非发现率方面有明显的改善。本方法也通过与领先的多种测试方法进行比较来评估。
The impact of dependence between individual test statistics is currently among the most discussed topics in the multiple testing of high-dimensional data literature, especially since Benjamini and Hochberg (1995) introduced the false discovery rate (FDR). Many papers have first focused on the impact of dependence on the control of the FDR. Sonic more recent works have investigated approaches that account for common information shared by all the variables to stabilize the distribution of the error rates. Similarly, we propose to model this sharing of information by a factor analysis structure for the conditional variance of the test statistics. It is shown that the variance of the number of false discoveries increases along with the fraction of common variance. Test statistics for general linear contrasts are deduced, taking advantage of the common factor structure to reduce the variance of the error rates, A conditional FDR estimate is proposed and the overall performance of multiple testing procedure is shown to be markedly improved, regarding the nondiscovery rate, with respect to classical procedures. The present methodology is also assessed by comparison with leading multiple testing methods.