标题： 基于模型的条件特征筛选与曝光变量 显示英文标题

标题： Model-Free Conditional Feature Screening with Exposure Variables

摘要： 在高维分析中，解释变量对响应变量的影响有时依赖于某些暴露变量（如时间或环境因素）。本文中，为了刻画每个预测变量的重要性，我们利用其在给定暴露变量条件下的条件相关性与响应变量的经验分布函数。基于这一思想，随后提出了一种无模型条件筛选方法，旨在识别那些效应可能随暴露变量变化的显著预测变量。所提出的筛选程序适用于任意模型形式，包括具有异方差性的模型（其中方差成分也可能随暴露变量变化）的情况。该方法对极端值或异常值也具有鲁棒性。在一些温和条件下，我们建立了该筛选方法的理想确定性和排名一致性属性。通过模拟研究和乳腺癌数据集的应用，展示了有限样本下的性能。 显示英文摘要

摘要： In high dimensional analysis, effects of explanatory variables on responses sometimes rely on certain exposure variables, such as time or environmental factors. In this paper, to characterize the importance of each predictor, we utilize its conditional correlation given exposure variables with the empirical distribution function of response. A model-free conditional screening method is subsequently advocated based on this idea, aiming to identify significant predictors whose effects may vary with the exposure variables. The proposed screening procedure is applicable to any model form, including that with heteroscedasticity where the variance component may also vary with exposure variables. It is also robust to extreme values or outlier. Under some mild conditions, we establish the desirable sure screening and the ranking consistency properties of the screening method. The finite sample performances are illustrated by simulation studies and an application to the breast cancer dataset.