The Kolmogorov filter for variable screening in high-dimensional binary classification
The Kolmogorov filter for variable screening in high-dimensional binary classification
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DOI：
10.1093/biomet/ass062
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发表时间：
2013-03-01
期刊：
影响因子：
2.7
通讯作者：
Zou, Hui
中科院分区：
文献类型：
--
作者：
Mai, Qing;Zou, Hui
已经提出了变量筛选技术来减轻分类问题中高维的影响，包括t检验边缘筛选（Fan & Fan，2008）和最大边缘似然筛选（Fan & Song，2010）。然而，这些方法依赖于很强的建模假设，在真实的应用中很容易被违反。为了规避参数建模假设，我们提出了一种新的变量筛选技术的基础上的Kolmogorov-Smirnov统计的二进制分类。我们证明了这种所谓的Kolmogorov滤波器在非常弱的模型假设下具有一定的屏蔽特性。我们补充我们的理论研究的模拟研究。
Variable screening techniques have been proposed to mitigate the impact of high dimensionality in classification problems, including t-test marginal screening (Fan & Fan, 2008) and maximum marginal likelihood screening (Fan & Song, 2010). However, these methods rely on strong modelling assumptions that are easily violated in real applications. To circumvent the parametric modelling assumptions, we propose a new variable screening technique for binary classification based on the Kolmogorov-Smirnov statistic. We prove that this so-called Kolmogorov filter enjoys the sure screening property under much weakened model assumptions. We supplement our theoretical study by a simulation study.