论文标题
与基于竞争的程序进行可变选择的本地错误发现率估计
Local False Discovery Rate Estimation with Competition-Based Procedures for Variable Selection
论文作者
论文摘要
多个假设检验已广泛应用于处理高维数据的问题，例如选择重要变量并控制选择错误率。多个假设检验中使用的错误率最高的量度是错误的发现率（FDR）。近年来，当地的错误发现率（FDR）引起了人们对单个假设的信心的优势，引起了很多关注。但是，大多数方法通过p值或统计数据估算了FDR，并具有已知的空分布，这些分布有时是不可用或可靠的。本文采用了基于竞争程序的程序的创新方法，例如仿基滤波器，提出了一种名为TDFDR的新方法，该方法归因于局部错误的发现率估计，该方法不含p值或已知的无效分布。仿真结果表明，TDFDR可以通过两个基于竞争的程序准确地估算FDR。在实际数据分析中，在两个生物数据集上验证了TDFDR在变量选择上的功能。
Multiple hypothesis testing has been widely applied to problems dealing with high-dimensional data, e.g., selecting significant variables and controlling the selection error rate. The most prevailing measure of error rate used in the multiple hypothesis testing is the false discovery rate (FDR). In recent years, local false discovery rate (fdr) has drawn much attention, due to its advantage of accessing the confidence of individual hypothesis. However, most methods estimate fdr through p-values or statistics with known null distributions, which are sometimes not available or reliable. Adopting the innovative methodology of competition-based procedures, e.g., knockoff filter, this paper proposes a new approach, named TDfdr, to local false discovery rate estimation, which is free of the p-values or known null distributions. Simulation results demonstrate that TDfdr can accurately estimate the fdr with two competition-based procedures. In real data analysis, the power of TDfdr on variable selection is verified on two biological datasets.
