Previous |  Up |  Next

Article

Title: A homogeneity test of large dimensional covariance matrices under non-normality (English)
Author: Ahmad, M. Rauf
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 54
Issue: 5
Year: 2018
Pages: 908-920
Summary lang: English
.
Category: math
.
Summary: A test statistic for homogeneity of two or more covariance matrices is presented when the distributions may be non-normal and the dimension may exceed the sample size. Using the Frobenius norm of the difference of null and alternative hypotheses, the statistic is constructed as a linear combination of consistent, location-invariant, estimators of trace functions that constitute the norm. These estimators are defined as $U$-statistics and the corresponding theory is exploited to derive the normal limit of the statistic under a few mild assumptions as both sample size and dimension grow large. Simulations are used to assess the accuracy of the statistic. (English)
Keyword: high-dimensional inference
Keyword: covariance testing
Keyword: $U$-statistics
Keyword: non-normality
MSC: 62H15
idZBL: Zbl 07031751
idMR: MR3893127
DOI: 10.14736/kyb-2018-5-0908
.
Date available: 2018-12-10T10:12:42Z
Last updated: 2020-01-05
Stable URL: http://hdl.handle.net/10338.dmlcz/147534
.
Reference: [1] Ahmad, M. R.: Location-invariant multi-sample $U$-tests for covariance matrices with large dimension..Scand. J. Stat. 44 (2017b), 500-523. MR 3658524, 10.1111/sjos.12262
Reference: [2] Ahmad, M. R.: Testing homogeneity of several covariance matrices and multi-sample sphericity for high-dimensional data under non-normality..Comm. Stat. Theory Methods 46 (2017a), 3738-3753. MR 3590835, 10.1080/03610926.2015.1073310
Reference: [3] Ahmad, M. R.: On testing sphericity and identity of a covariance matrix with large dimensions..Math. Meth. Stat. 25 (2016), 121-132. MR 3519645, 10.3103/s1066530716020034
Reference: [4] Anderson, T.W.: An Introduction to Multivariate Statistical Analysis. Third edition..Wiley, NY 2003. MR 1990662
Reference: [5] Aoshima, M., Yata, K.: Two-stage procedures for high-dimensional data..Seq. An. 30 (2011), 356-399. MR 2855952, 10.1080/07474946.2011.619088
Reference: [6] Cai, T., Liu, W., Xia, Y.: Two-sample covariance matrix testing and support recovery in high-dimensional and sparse settings..J. Amer. Statist. Assoc. 108 (2013), 265-277. MR 3174618, 10.1080/01621459.2012.758041
Reference: [7] Fujikoshi, Y., Ulyanov, V. V., Shimizu, R.: Multivariate statistics: High-dimensional and large-sample approximations..Wiley, NY 2010. MR 2640807
Reference: [8] Hájek, J., Šidák, Z., Sen, P. K.: Theory of Rank Tests..Academic Press, SD 1999. Zbl 0944.62045, MR 1680991
Reference: [9] Kim, T. Y., Luo, Z-M., Kim, C.: The central limit theorem for degenerate variable $U$-statistics under dependence..J. Nonparam. Stat. 23 (2011), 683-699. MR 2836284, 10.1080/10485252.2011.556193
Reference: [10] Koroljuk, V. S., Borovskich, Y. V.: Theory of $U$-statistics..Kluwer Academic Press, Dordrecht 1994. MR 1472486, 10.1007/978-94-017-3515-5
Reference: [11] Lee, A. J.: $U$-statistics: Theory and Practice..CRC Press, Boca Raton 1990.
Reference: [12] Lehmann, E. L.: Elements of Large-sample Theory..Springer, NY 1999. MR 1663158, 10.1007/b98855
Reference: [13] Li, J., Chen, S. X.: Two sample tests for high-dimensional covariance matrices..Ann. Stat. 40 (2012), 908-940. MR 2985938, 10.1214/12-aos993
Reference: [14] Liu, B., Xu, L., Zheng, S., Tian, G-L.: A new test for the proportionality of two large-dimensional covariance matrices..J. Multiv. An. 131 (2014), 293-308. MR 3252651, 10.1016/j.jmva.2014.06.008
Reference: [15] Mikosch, T.: Weak invariance principles for weighted $U$-statistics..J. Theoret. Prob. 7 (1991), 147-173. MR 1256396, 10.1007/bf02213365
Reference: [16] Mikosch, T.: A weak invariance principle for weighted $U$-statistics with varying kernels..J. Multiv. An. 47 (1993), 82-102. MR 1239107, 10.1006/jmva.1993.1072
Reference: [17] Muirhead, R. J.: Aspects of Multivariate Statistical Theory..Wiley, NY 2005 MR 0652932, 10.1002/9780470316559
Reference: [18] Pinheiro, A., Sen, K., Pinheiro, H. P.: Decomposibility of high-dimensional diversity measures: Quasi-$U$-statistics, martigales, and nonstandard asymptotics..J. Multiv. An. 100 (2009), 1645-1656. MR 2535376, 10.1016/j.jmva.2009.01.007
Reference: [19] Qiu, Y., Chen, S. X.: Test for bandedness of high-dimensional covariance matrices and bandwidth estimation..Ann. Stat. 40 (2012) 1285-1314. MR 3015026, 10.1214/12-aos1002
Reference: [20] Schott, J. R.: A test for the equality of covariance matrices when the dimension is large relative to the sample size..Computat. Statist. Data Analysis 51 (2007), 6535-6542. MR 2408613, 10.1016/j.csda.2007.03.004
Reference: [21] Seber, G. A. F.: Multivariate Observations..Wiley, NY 2004. MR 0746474, 10.1002/9780470316641
Reference: [22] Sen, P. K.: Robust statistical inference for high-dimensional data models with applications in genomics..Aust. J. Stat. 35 (2006), 197-214.
Reference: [23] Serfling, R. J.: Approximation Theorems of Mathematical Statistics..Wiley, Weinheim 1980. MR 0595165, 10.1002/9780470316481
Reference: [24] Srivastava, M. S., Yanagihara, H.: Testing the equality of several covariance matrices with fewer observations than the dimension..J. Multiv. An. 101, 1319-1329. MR 2609494, 10.1016/j.jmva.2009.12.010
Reference: [25] Vaart, A. W. van der: Asymptotic Statistics..Cambridge University Press, 1998. MR 1652247, 10.1017/cbo9780511802256
Reference: [26] Zhong, P-S., Chen, S. X.: Tests for high-dimensional regression coefficients with factorial designs..J. Amer. Statist. Assoc. 106 (2011), 260-274. MR 2816719, 10.1198/jasa.2011.tm10284
.

Files

Files Size Format View
Kybernetika_54-2018-5_3.pdf 535.2Kb application/pdf View/Open
Back to standard record
Partner of
EuDML logo