Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis.
Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis.
复制标题
用于超高维判别分析的无模型特征筛选
DOI：
10.1080/01621459.2014.920256
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发表时间：
2015-06-01
影响因子：
3.7
通讯作者：
Zhong W
中科院分区：
文献类型：
--
作者：
Cui H;Li R;Zhong W
这项工作涉及超高维判别分析的边际确定独立特征筛选。响应变量在判别分析中是分类的。这使得我们能够利用条件分布函数构建新的索引来进行特征筛选。在本文中，我们提出了一种基于经验条件分布函数的边缘特征筛选程序。我们为所提出的程序建立了确定的筛选和排名一致性属性，而不假设预测变量的任何矩条件。拟议的程序有几个吸引人的优点。首先，它是无模型的，因为它的实现不需要回归模型的规范。其次，它对预测变量的重尾分布和潜在异常值的存在具有鲁棒性。第三，它允许分类响应具有 O(nκ) 数量级的不同类别数，且某些 κ ⩾ 0。我们通过蒙特卡洛模拟研究和数值比较来评估所提出的程序的有限样本属性。我们通过对两个现实数据集的实证分析进一步说明了所提出的方法。本文的补充材料可在线获取。
This work is concerned with marginal sure independence feature screening for ultrahigh dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening. In this article, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency properties for the proposed procedure without assuming any moment condition on the predictors. The proposed procedure enjoys several appealing merits. First, it is model-free in that its implementation does not require specification of a regression model. Second, it is robust to heavy-tailed distributions of predictors and the presence of potential outliers. Third, it allows the categorical response having a diverging number of classes in the order of O(nκ) with some κ ⩾ 0. We assess the finite sample property of the proposed procedure by Monte Carlo simulation studies and numerical comparison. We further illustrate the proposed methodology by empirical analyses of two real-life datasets. Supplementary materials for this article are available online.