Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
任意协方差依赖下的错误发现比例估计AI 解读 ↓
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任意协方差依赖下的错误发现比例估计
- 研究问题
- 大规模多重检验统计量相互相关时，如何估计一次实际检验中的错误发现比例，并利用依赖信息改进检验？
- 方法与思路
- 作者通过主因子近似去除共同依赖，推导统一阈值下FDP的近似表达式与一致估计，并提出依赖调整程序。
- 材料陈述的贡献
- 预印本摘要称，方法能估计实际FDP，模拟中与既有方法比较有利，依赖调整程序的功效高于固定阈值程序。
- 阅读关注点
- 应核对主因子近似的条件、剩余相关性的控制、一致性适用范围，以及估计FDP如何衔接错误发现控制。
- 阅读边界
- 仅据arXiv 1010.6056v2预印本摘要；不能据此声称已同行评审或期刊接收，也不推断刊本变化；摘要未列完整一致性条件。
原文摘要
Multiple hypothesis testing is a fundamental problem in high dimensional inference, with wide applications in many scientific fields. In genome-wide association studies, tens of thousands of tests are performed simultaneously to find if any SNPs are associated with some traits and those tests are correlated. When test statistics are correlated, false discovery control becomes very challenging under arbitrary dependence. In the current paper, we propose a novel method based on principal factor approximation, which successfully subtracts the common dependence and weakens significantly the correlation structure, to deal with an arbitrary dependence structure. We derive an approximate expression for false discovery proportion (FDP) in large scale multiple testing when a common threshold is used and provide a consistent estimate of realized FDP. This result has important applications in controlling FDR and FDP. Our estimate of realized FDP compares favorably with Efron (2007)'s approach, as demonstrated in the simulated examples. Our approach is further illustrated by some real data applications. We also propose a dependence-adjusted procedure, which is more powerful than the fixed threshold procedure.