Simultaneously finding active predictors and controlling the false discovery rate(FDR)for high-dimensional survival data is an important but challenging statistical problem.In this paper,the authors propose a novel va...Simultaneously finding active predictors and controlling the false discovery rate(FDR)for high-dimensional survival data is an important but challenging statistical problem.In this paper,the authors propose a novel variable selection procedure with error rate control for the high-dimensional Cox model.By adopting a data-splitting strategy,the authors construct a series of symmetric statistics and then utilize the symmetry property to derive a data-driven threshold to achieve error rate control.The authors establish finite-sample and asymptotic FDR control results under some mild conditions.Simulation results as well as a real data application show that the proposed approach successfully controls FDR and is often more powerful than the competing approaches.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.12301364,12322112,12071038,11925106,12231011,11931001,12226007,12326325,and 12131006the National Key R&D Program of China under Grant Nos.2022YFA1003703 and 2022YFA1003800+3 种基金the Natural Science Foundation of Anhui Province under Grant No.2308085QA09the Fundamental Research Funds for the Central Universities under Grant No.2243200006the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences under Grant No.CI2023C063YLLthe University Grant Council of Hong Kong。
文摘Simultaneously finding active predictors and controlling the false discovery rate(FDR)for high-dimensional survival data is an important but challenging statistical problem.In this paper,the authors propose a novel variable selection procedure with error rate control for the high-dimensional Cox model.By adopting a data-splitting strategy,the authors construct a series of symmetric statistics and then utilize the symmetry property to derive a data-driven threshold to achieve error rate control.The authors establish finite-sample and asymptotic FDR control results under some mild conditions.Simulation results as well as a real data application show that the proposed approach successfully controls FDR and is often more powerful than the competing approaches.
