A distributed multiple sample testing for massive data

doi:10.1080/02664763.2021.1911967

	A distributed multiple sample testing for massive data
	Xie Xiaoyue 1,3; Shi Jian 1,3; Song Kai 2
刊名	JOURNAL OF APPLIED STATISTICS
	2021-04-08
页码	19
关键词	Distributed scheme hypothesis testing fraud detection classification
ISSN号	0266-4763
DOI	10.1080/02664763.2021.1911967
英文摘要	When the data are stored in a distributed manner, direct application of traditional hypothesis testing procedures is often prohibitive due to communication costs and privacy concerns. This paper mainly develops and investigates a distributed two-node Kolmogorov-Smirnov hypothesis testing scheme, implemented by the divide-and-conquer strategy. In addition, this paper also provides a distributed fraud detection and a distribution-based classification for multi-node machines based on the proposed hypothesis testing scheme. The distributed fraud detection is to detect which node stores fraud data in multi-node machines and the distribution-based classification is to determine whether the multi-node distributions differ and classify different distributions. These methods can improve the accuracy of statistical inference in a distributed storage architecture. Furthermore, this paper verifies the feasibility of the proposed methods by simulation and real example studies.
WOS研究方向	Mathematics
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000637242100001
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/58423]
专题	中国科学院数学与系统科学研究院
通讯作者	Shi Jian
作者单位	1.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China 2.Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Xie Xiaoyue,Shi Jian,Song Kai. A distributed multiple sample testing for massive data[J]. JOURNAL OF APPLIED STATISTICS,2021:19.
APA	Xie Xiaoyue,Shi Jian,&Song Kai.(2021).A distributed multiple sample testing for massive data.JOURNAL OF APPLIED STATISTICS,19.
MLA	Xie Xiaoyue,et al."A distributed multiple sample testing for massive data".JOURNAL OF APPLIED STATISTICS (2021):19.