He Kevin, Zhou Xiang, Jiang Hui, Wen Xiaoquan, Li Yi
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
University of Michigan, Center for Computational Medicine and Bioinformatics, Ann Arbor, MI, USA.
Stat Appl Genet Mol Biol. 2018 Dec 15;17(6):/j/sagmb.2018.17.issue-6/sagmb-2018-0038/sagmb-2018-0038.xml. doi: 10.1515/sagmb-2018-0038.
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors much exceeding the sample size. Penalized variable selection has emerged as a powerful and efficient dimension reduction tool. However, control of false discoveries (i.e. inclusion of irrelevant variables) for penalized high-dimensional variable selection presents serious challenges. To effectively control the fraction of false discoveries for penalized variable selections, we propose a false discovery controlling procedure. The proposed method is general and flexible, and can work with a broad class of variable selection algorithms, not only for linear regressions, but also for generalized linear models and survival analysis.
现代生物技术产生了大量的高通量数据，预测变量的数量远远超过样本量。惩罚变量选择已成为一种强大而有效的降维工具。然而，对于惩罚高维变量选择而言，控制错误发现（即包含无关变量）面临严峻挑战。为了有效控制惩罚变量选择中的错误发现比例，我们提出了一种错误发现控制程序。所提出的方法具有通用性和灵活性，可与广泛的变量选择算法配合使用，不仅适用于线性回归，还适用于广义线性模型和生存分析。
