Model-Free Conditional Feature Screening with FDR Control
提出一种无需模型假设的条件特征筛选方法，能控制错误发现率，适用于超高维数据，对厚尾分布稳健，并通过模拟和实际数据验证了有效性。
In this article, we propose a model-free conditional feature screening method with false discovery rate (FDR) control for ultra-high dimensional data. The proposed method is built upon a new measure of conditional independence. Thus, the new method does not require a specific functional form of the regression function and is robust to heavy-tailed responses and predictors. The variables to be conditional on are allowed to be multivariate. The proposed method enjoys sure screening and ranking consistency properties under mild regularity conditions. To control the FDR, we apply the Reflection via Data Splitting method and prove its theoretical guarantee using martingale theory and empirical process techniques. Simulated examples and real data analysis show that the proposed method performs very well compared with existing works. Supplementary materials for this article are available online.