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	Generalized T (3)-plot for testing high-dimensional normality
	Ai, Mingyao ; Liang, Jiajuan ; Tang, Man-Lai
	2016
关键词	Dimension reduction graphical method high-dimensional data multivariate normality spherical distribution DETECT NON-MULTINORMALITY MULTIVARIATE EFFICIENCY PORTFOLIO PLOTS
英文摘要	A new dimension-reduction graphical method for testing high-dimensional normality is developed by using the theory of spherical distributions and the idea of principal component analysis. The dimension reduction is realized by projecting high-dimensional data onto some selected eigenvector directions. The asymptotic statistical independence of the plotting functions on the selected eigenvector directions provides the principle for the new plot. A departure from multivariate normality of the raw data could be captured by at least one plot on the selected eigenvector direction. Acceptance regions associated with the plots are provided to enhance interpretability of the plots. Monte Carlo studies and an illustrative example show that the proposed graphical method has competitive power performance and improves the existing graphical method significantly in testing high-dimensional normality.; National Natural Science Foundation of China [11271032, 11331011]; BCMIIS; LMEQF; SCI(E); 中国科技核心期刊(ISTIC); ARTICLE; myai@math.pku.edu.cn; 6; 1363-1378; 11
语种	英语
出处	SCI
出版者	FRONTIERS OF MATHEMATICS IN CHINA
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/457472]
专题	数学科学学院
推荐引用方式 GB/T 7714	Ai, Mingyao,Liang, Jiajuan,Tang, Man-Lai. Generalized T (3)-plot for testing high-dimensional normality. 2016-01-01.

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