A necessary test for complete independence in high dimensions using rank-correlations

Journal of Multivariate Analysis

Volumn 121, Issue , 2013, Pages 224-232

  Wang, Guanghui ^a   Zou, Changliang ^a   Wang, Zhaojun ^a  

^a NANKAI UNIVERSITY   (China)

Author keywords

Asymptotic normality; Complete independence; High dimensional problem; Necessary tests; Spearman's rank correlation

Indexed keywords

EID: 84882695031     PISSN: 0047259X     EISSN: 10957243     Source Type: Journal    
DOI: 10.1016/j.jmva.2013.05.014     Document Type: Article

References (12)

1. Bai Z.D., Saranadasa H. Effect of high dimension: by an example of a two sample problem. Statist. Sinica 1996, 6:311-329.
2. Chen S.X., Qin Y.L. A two-sample test for high-dimensional data with applications to gene-set testing. Ann. Statist. 2010, 38:808-835.
3. David S.T., Kendall M.G., Stuart A. Some questions of distribution in the theory of rank correlation. Biometrika 1951, 38:131-140.
4. Fan J.Q., Peng H., Huang T. Semilinear high-dimensional model for normalization of microarray data: a theoretical analysis and partial consistency. J. Amer. Statist. Assoc. 2005, 100:781-796.
5. Hall P., Heyde C.C. Martingale Limit Theory and its Applications 1980, Academic Press, New York.
6. Ledoit O., Wolf M. Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size. Ann. Statist. 2002, 30:1081-1102.
7. Liang J.J., Fang K.T., Hickernell F.J. Some necessary uniform tests for spherical symmetry. Ann. Inst. Statist. Math. 2008, 60:679-696.
8. Schott J.R. Testing for complete independence in high dimensions. Biometrika 2005, 92:951-956.
9. Spearman C. The proof and measurement of association between two things. Amer. J. Psychol. 1904, 15:72-101.
10. Srivastava M.S. Some tests concerning the covariance matrix in high-dimensional data. J. Japan Statist. Soc. 2005, 35:251-272.
11. Srivastava M.S. Some tests criteria for the covariance matrix with fewer observations than the dimension. Acta Comment. Univ. Tartu. Math. 2006, 10:77-93.
12. Srivastava M.S., Kollo T., von Rosen D. Some tests for the covariance matrix with fewer observations than the dimension under nonnormality. J. Multivariate Anal. 2011, 102:1090-1103.