Screen then select: a strategy for correlated predictors in high-dimensional quantile regression

Strong correlation among predictors and heavy-tailed noises pose a great challenge in the analysis of ultra-high dimensional data. Such challenge leads to an increase in the computation time for discovering active variables and a decrease in selection accuracy. To address this issue, we propose an innovative two-stage screen-then-select approach and its derivative procedure based on a robust quantile regression with sparsity assumption. This approach initially screens important features by ranking quantile ridge estimation and subsequently employs a likelihood-based post-screening selection strategy to refine variable selection. Additionally, we conduct an internal competition mechanism along the greedy search path to enhance the robustness of algorithm against the design dependence. Our methods are simple to implement and possess numerous desirable properties from theoretical and computational standpoints. Theoretically, we establish the strong consistency of feature selection for the proposed methods under some regularity conditions. In empirical studies, we assess the finite sample performance of our methods by comparing them with utility screening approaches and existing penalized quantile regression methods. Furthermore, we apply our methods to identify genes associated with anticancer drug sensitivities for practical guidance.