Reproducible feature selection in high-dimensional accelerated failure time models

被引：4

作者：

Dong, Yan ^{[1
]}

Li, Daoji ^{[2
]}

Zheng, Zemin ^{[1
]}

Zhou, Jia ^{[1
]}

机构：

[1] Univ Sci & Technol China, Int Inst Finance, Sch Management, Hefei 230026, Peoples R China

[2] Calif State Univ, Dept Informat Syst & Decis Sci, Fullerton, CA 92831 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2022年 / 181卷

基金：

中国国家自然科学基金;

关键词：

Feature selection; Accelerated failure time models; False discovery rate; High dimensionality; Knockoffs; FALSE DISCOVERY RATE;

D O I：

10.1016/j.spl.2021.109275

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We propose a new feature selection procedure with guaranteed FDR control for high-dimensional AFT models, which is among the first attempts of reproducible learning in survival analysis. The effectiveness of the proposed method is theoretically and numerically demonstrated. (C) 2021 Elsevier B.V. All rights reserved.

引用

页数：6

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