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A Two-Sample Test for Equality of Means in High Dimension

Author

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  • Karl Bruce Gregory
  • Raymond J. Carroll
  • Veerabhadran Baladandayuthapani
  • Soumendra N. Lahiri

Abstract

We develop a test statistic for testing the equality of two population mean vectors in the "large- p -small- n " setting. Such a test must surmount the rank-deficiency of the sample covariance matrix, which breaks down the classic Hotelling T -super-2 test. The proposed procedure, called the generalized component test, avoids full estimation of the covariance matrix by assuming that the p components admit a logical ordering such that the dependence between components is related to their displacement. The test is shown to be competitive with other recently developed methods under ARMA and long-range dependence structures and to achieve superior power for heavy-tailed data. The test does not assume equality of covariance matrices between the two populations, is robust to heteroscedasticity in the component variances, and requires very little computation time, which allows its use in settings with very large p . An analysis of mitochondrial calcium concentration in mouse cardiac muscles over time and of copy number variations in a glioblastoma multiforme dataset from The Cancer Genome Atlas are carried out to illustrate the test. Supplementary materials for this article are available online.

Suggested Citation

  • Karl Bruce Gregory & Raymond J. Carroll & Veerabhadran Baladandayuthapani & Soumendra N. Lahiri, 2015. "A Two-Sample Test for Equality of Means in High Dimension," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 837-849, June.
    • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:510:p:837-849
    DOI: 10.1080/01621459.2014.934826
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    Citations

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    Cited by:

    1. Zhang, Huaiyu & Wang, Haiyan, 2021. "A more powerful test of equality of high-dimensional two-sample means," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    2. Tzviel Frostig & Yoav Benjamini, 2022. "Testing the equality of multivariate means when $$p>n$$ p > n by combining the Hotelling and Simes tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 390-415, June.
    3. Zhang, Jin-Ting & Guo, Jia & Zhou, Bu, 2017. "Linear hypothesis testing in high-dimensional one-way MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 200-216.
    4. Li, Yang & Wang, Zhaojun & Zou, Changliang, 2016. "A simpler spatial-sign-based two-sample test for high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 192-198.
    5. Jiang, Qing & Hušková, Marie & Meintanis, Simos G. & Zhu, Lixing, 2019. "Asymptotics, finite-sample comparisons and applications for two-sample tests with functional data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 202-220.
    6. Hyodo, Masashi & Watanabe, Hiroki & Seo, Takashi, 2018. "On simultaneous confidence interval estimation for the difference of paired mean vectors in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 160-173.
    7. Feng, Long & Sun, Fasheng, 2015. "A note on high-dimensional two-sample test," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 29-36.
    8. Yuanyuan Jiang & Xingzhong Xu, 2022. "A Two-Sample Test of High Dimensional Means Based on Posterior Bayes Factor," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    9. Tao Zhang & Zhiwen Wang & Yanling Wan, 2021. "Functional test for high-dimensional covariance matrix, with application to mitochondrial calcium concentration," Statistical Papers, Springer, vol. 62(3), pages 1213-1230, June.
    10. Zhengbang Li & Fuxiang Liu & Luanjie Zeng & Guoxin Zuo, 2021. "A stationary bootstrap test about two mean vectors comparison with somewhat dense differences and fewer sample size than dimension," Computational Statistics, Springer, vol. 36(2), pages 941-960, June.
    11. Ghosh, Santu & Ayyala, Deepak Nag & Hellebuyck, Rafael, 2021. "Two-sample high dimensional mean test based on prepivots," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    12. Ouyang, Yanyan & Liu, Jiamin & Tong, Tiejun & Xu, Wangli, 2022. "A rank-based high-dimensional test for equality of mean vectors," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    13. Li, Jun, 2023. "Finite sample t-tests for high-dimensional means," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    14. Harrar, Solomon W. & Kong, Xiaoli, 2022. "Recent developments in high-dimensional inference for multivariate data: Parametric, semiparametric and nonparametric approaches," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    15. Huang, Yuan & Li, Changcheng & Li, Runze & Yang, Songshan, 2022. "An overview of tests on high-dimensional means," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    16. Dette, Holger & Dörnemann, Nina, 2020. "Likelihood ratio tests for many groups in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 178(C).

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