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.

中文翻译：

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具有高维协变量的惩罚变量选择的错误发现控制。


现代生物技术已经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且高效的降维工具。然而，控制惩罚性高维变量选择的错误发现（即包含不相关变量）提出了严峻的挑战。为了有效控制惩罚变量选择的错误发现比例，我们提出了一种错误发现控制程序。所提出的方法通用且灵活，并且可以与广泛的变量选择算法一起使用，不仅适用于线性回归，而且适用于广义线性模型和生存分析。