Testing the statistical significance of an ultra-high-dimensional naive Bayes classifier

被引：0
|
作者：
An, Baiguo [1 ]
Wang, Hansheng [1 ]
Guo, Jianhua [1 ]
机构：
[1] Peking Univ, Guanghua Sch Management, Beijing, Peoples R China
来源：
STATISTICS AND ITS INTERFACE | 2013年 / 6卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Binary Predictor; Hypothesis Testing; Naive Bayes; Supervised Learning; Text Classification; Ultra-High-Dimensional Data; SELECTION;
D O I：
暂无
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
The naive Bayes approach is one of the most popular methods used for classification. Nevertheless, how to test its statistical significance under an ultra-high-dimensional (UHD) setup is not well understood. To fill this important theoretical gap, we propose a novel testing statistic with a standard normal asymptotic null distribution, even if the predictor dimension is considerably larger than the sample size. This makes the proposed method useful for UHD data analysis. Simulation studies are presented to demonstrate its finite sample performance and a text classification example is described for illustration.
引用
收藏
页码：223 / 229
页数：7
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