Model-free Feature Screening and False Discovery Control for High-dimensional Quantile Regressions
高维分位数回归的无模型特征筛选与错误发现控制
High-dimensional quantile regressions are powerful tools for capturing the diverse relationships between predictors and response variables across different quantile levels. However, existing feature screening methods for quantile regressions often rely on restrictive model assumptions, limiting their applicability in complex data settings. In this paper, we propose a novel model-free feature screening framework for high-dimensional quantile regressions, leveraging a novel…