南方科技大学知识苑(SUSTech KC): Global one-sample tests for high-dimensional covariance matrices

题名	Global one-sample tests for high-dimensional covariance matrices
作者	Wang，Xiaoyi 1; Liu，Baisen 2; Shi，Ning Zhong 1; Tian，Guo Liang3 ; Zheng，Shurong 1
发表日期	2021
DOI	10.1080/00949655.2021.1882459
发表期刊	JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 影响因子和分区
ISSN	0094-9655
EISSN	1563-5163
摘要	Testing high-dimensional covariance matrix plays an important role in multivariate statistical analysis. Many statisticians used the statistics based on (Formula presented.) to test (Formula presented.) with (Formula presented.) being the covariance matrix. However, none have proposed a statistic based on (Formula presented.) for this purpose. In fact, neither of the two tests is superior to the other based on their powers because they target different kinds of dense alternatives. Furthermore, some maximum-type tests were proposed to accommodate sparse alternatives. By using the advantages of these tests, we propose two new tests for one-sample covariance matrix when the sample size and data dimension increase proportionally. One is suitable for dense alternatives, the other is powerful against a wide range of situations, such as dense, sparse or a mixture of both alternatives. Extensive simulation results show that our proposed tests maintain high powers against various alternatives while the existing tests fail in at least one situation.
关键词	Dense alternative high dimension random matrix theory sparse alternative
相关链接	[Scopus记录]
收录类别	SCI
语种	英语
学校署名	其他
资助项目	NSFC[12071066,11690012] ; Liaoning Provincial Education Department, China[LN2020J18]
WOS研究方向	Computer Science ; Mathematics
WOS类目	Computer Science, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:000627184800001
出版者	TAYLOR & FRANCIS LTD
ESI学科分类	MATHEMATICS
Scopus记录号	2-s2.0-85102364590
来源库	Scopus
引用统计	被引频次[WOS]：1
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/221743
专题	理学院_统计与数据科学系
作者单位	1.KLAS and School of Mathematics and Statistics,Northeast Normal University,Changchun,China 2.School of Statistics,Dongbei University of Finance Economics,Dalian,China 3.Department of Statistics and Data Science,Southern University of Science and Technology,Shenzhen,China
推荐引用方式 GB/T 7714	Wang，Xiaoyi,Liu，Baisen,Shi，Ning Zhong,et al. Global one-sample tests for high-dimensional covariance matrices[J]. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION,2021.
APA	Wang，Xiaoyi,Liu，Baisen,Shi，Ning Zhong,Tian，Guo Liang,&Zheng，Shurong.(2021).Global one-sample tests for high-dimensional covariance matrices.JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION.
MLA	Wang，Xiaoyi,et al."Global one-sample tests for high-dimensional covariance matrices".JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION (2021).