Two-sample tests for sparse high-dimensional binary data

作     者：Plunkett, Amanda Park, Junyong 

作者机构：Dept Def Ft George G Meade MD USA Univ Maryland Baltimore Cty Dept Math & Stat Baltimore MD 21250 USA 

出 版 物：《COMMUNICATIONS IN STATISTICS-THEORY AND METHODS》 (统计学通讯：理论与方法)

年 卷 期：2017年第46卷第22期

页      面：11181-11193页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：U.S. National Science Foundation through the MRI program [CNS-0821258] SCREMS [DMS-0821311] University of Maryland, Baltimore County (UMBC) 

主　　题：Dimension reduction High dimension Multivariate binary data 

摘      要：In this article, we study the methods for two-sample hypothesis testing of high-dimensional data coming from a multivariate binary distribution. We test the random projection method and apply an Edgeworth expansion for improvement. Additionally, we propose new statistics which are especially useful for sparse data. We compare the performance of these tests in various scenarios through simulations run in a parallel computing environment. Additionally, we apply these tests to the 20 Newsgroup data showing that our proposed tests have considerably higher power than the others for differentiating groups of news articles with different topics.