High dimensional controlled variable selection with model-X knockoffs in the AFT model

Abstract

Interpretability and stability are two important characteristics required for the application of high dimensional data in statistics. Although the former has been favored by many existing forecasting methods to some extent, the latter in the sense of controlling the fraction of wrongly discovered features is still largely underdeveloped. Under the accelerated failure time model, this paper introduces a controlled variable selection method with the general framework of Model-X knockoffs to tackle high dimensional data. We provide theoretical justifications on the asymptotic false discovery rate (FDR) control. The proposed method has attracted significant interest due to its strong control of the FDR while preserving predictive power. Several simulation examples are conducted to assess the finite sample performance with desired interpretability and stability. A real data example from Acute Myeloid Leukemia study is analyzed to demonstrate the utility of the proposed method in practice.