This paper is concerned with feature screening for the ultrahigh dimensional discriminant analysis. A new feature screening procedure based on the conditional quantile is proposed. The proposed procedure has some desirable features. First, it is model-free which does not require specific discriminant model and can be directly applied to the multi-categories situation. Second, it is robust against heavy-tailed distributions, potential outliers and the sample shortage for some categories, which are very common for high dimensional data. We establish the sure screening property and ranking consistency property of the proposed procedure under some regular conditions. Simulation studies and a real data example are used to assess its finite sample performance.

中文翻译：

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用于超高维判别分析的稳健复合加权分位数筛选

本文关注的是超高维判别分析的特征筛选。提出了一种基于条件分位数的新特征筛选程序。所提议的程序具有一些理想的特征。首先，它是无模型的，不需要特定的判别模型，可以直接应用于多类别情况。其次，它对重尾分布、潜在异常值和某些类别的样本短缺具有鲁棒性，这在高维数据中非常常见。我们在一些规则条件下建立了所提出程序的确定筛选特性和排序一致性特性。模拟研究和真实数据示例用于评估其有限样本性能。