Data-Driven Nonparametric Existence and Association Problems

doi:10.1109/TSP.2018.2875392

	Data-Driven Nonparametric Existence and Association Problems
	Liu, Yixian1,2,3 ; Liang, Yingbin 4; Cui, Shuguang 5,6,7
	2018-12-15
发表期刊	IEEE TRANSACTIONS ON SIGNAL PROCESSING (IF:4.6[JCR-2023],5.2[5-Year])
ISSN	1053-587X
卷号	66 期号:24 页码:6377-6389
发表状态	已发表
DOI	10.1109/TSP.2018.2875392
摘要	We investigate two closely related nonparametric hypothesis testing problems. In the first problem (i.e., the existence problem), we test whether a testing data stream is generated by one of a set of composite distributions. In the second problem (i.e., the association problem), we test which one of the multiple distributions generates a testing data stream. We assume that some distributions in the set are unknown, and instead, only training sequences generated by the corresponding distributions are available. For both problems, we construct the generalized likelihood tests and characterize the error exponents of the maximum error probabilities. For the existence problem, we show that the error exponent is mainly captured by the Chernoff information between the set of composite distributions and alternative distributions. For the association problem, we show that the error exponent is captured by the minimum Chernoff information between each pair of distributions as well as the Kullback-Leibler Divergences between the approximated distributions (via training sequences) and the true distributions. We also show that the ratio between the lengths of training and testing sequences plays an important role in determining the error decay rate.
关键词	Multiple hypothesis testing binary composite hypothesis testing generalized likelihood test error exponent KL divergence
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收录类别	EI ; SCIE ; SCI
语种	英语
资助项目	Shenzhen Fundamental Research Fund[KQTD2015033114415450] ; Shenzhen Fundamental Research Fund[ZDSYS201707251409055]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000449396200004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号	20184606076259
EI主题词	Decay (organic) ; Probability ; Statistical tests
EI分类号	Biochemistry:801.2 ; Probability Theory:922.1 ; Mathematical Statistics:922.2
WOS关键词	DISTRIBUTIONS ; CLASSIFICATION ; CONVERGENCE ; TESTS
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/28719
专题	信息科学与技术学院_博士生
通讯作者	Liu, Yixian
作者单位	1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 200031, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai 200031, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100864, Peoples R China 4.Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA 5.Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China 6.Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China 7.Univ Calif Davis, Dept Elect & Comp Engn, Davis, CA 95616 USA
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Liu, Yixian,Liang, Yingbin,Cui, Shuguang. Data-Driven Nonparametric Existence and Association Problems[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING,2018,66(24):6377-6389.
APA	Liu, Yixian,Liang, Yingbin,&Cui, Shuguang.(2018).Data-Driven Nonparametric Existence and Association Problems.IEEE TRANSACTIONS ON SIGNAL PROCESSING,66(24),6377-6389.
MLA	Liu, Yixian,et al."Data-Driven Nonparametric Existence and Association Problems".IEEE TRANSACTIONS ON SIGNAL PROCESSING 66.24(2018):6377-6389.