Motivated by high-dimensional genomic studies, we develop an improved procedure for adaptive Lasso in high-dimensional survival analysis. The proposed procedure effectively reduces the false discoveries while successfully maintaining the false negative proportions, which improves the existing adaptive Lasso procedures. The implementation of the proposed procedure is straightforward and it is sufficiently flexible to accommodate large-scale problems where traditional procedures are impractical. To quantify the uncertainty of variable selection and control the family-wise error rate, a multiple sample-splitting based testing algorithm is developed. The practical utility of the proposed procedure are examined through simulation studies. The methods developed are then applied to a multiple myeloma data set.

中文翻译：

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一种改进的变量选择程序，用于高维生存分析中的自适应套索。

受高维基因组研究的推动，我们开发了一种用于高维生存分析的自适应套索的改进程序。所提出的程序有效地减少了错误的发现，同时成功地维持了错误的负比例，从而改善了现有的自适应套索程序。所提出的过程的实现是直接的，并且足够灵活以适应传统过程不可行的大规模问题。为了量化变量选择的不确定性并控制族错误率，开发了一种基于多样本分解的测试算法。通过仿真研究检查了所提出程序的实际实用性。然后将开发的方法应用于多发性骨髓瘤数据集。