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A high-dimension two-sample test for the mean using cluster subspaces

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  • Zhang, Jie
  • Pan, Meng

Abstract

A common problem in modern genetic research is that of comparing the mean vectors of two populations–typically in settings in which the data dimension is larger than the sample size–where Hotelling’s test cannot be applied.

Suggested Citation

  • Zhang, Jie & Pan, Meng, 2016. "A high-dimension two-sample test for the mean using cluster subspaces," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 87-97.
    • Handle: RePEc:eee:csdana:v:97:y:2016:i:c:p:87-97
    DOI: 10.1016/j.csda.2015.12.004
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    References listed on IDEAS

    as
    1. Thulin, Måns, 2014. "A high-dimensional two-sample test for the mean using random subspaces," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 26-38.
    2. Shen, Yanfeng & Lin, Zhengyan, 2015. "An adaptive test for the mean vector in large-p-small-n problems," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 25-38.
    3. Chen, Song Xi & Li, Jun & Zhong, Pingshou, 2014. "Two-Sample Tests for High Dimensional Means with Thresholding and Data Transformation," MPRA Paper 59815, University Library of Munich, Germany.
    4. Chen, Song Xi & Qin, Yingli, 2010. "A Two Sample Test for High Dimensional Data with Applications to Gene-set Testing," MPRA Paper 59642, University Library of Munich, Germany.
    5. T. Tony Cai & Weidong Liu & Yin Xia, 2014. "Two-sample test of high dimensional means under dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(2), pages 349-372, March.
    6. Srivastava, Muni S. & Du, Meng, 2008. "A test for the mean vector with fewer observations than the dimension," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 386-402, March.
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    Citations

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    Cited by:

    1. Wang, Wei & Lin, Nan & Tang, Xiang, 2019. "Robust two-sample test of high-dimensional mean vectors under dependence," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 312-329.
    2. 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.
    3. Qiu, Tao & Xu, Wangli & Zhu, Liping, 2021. "Two-sample test in high dimensions through random selection," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    4. Pini, Alessia & Stamm, Aymeric & Vantini, Simone, 2018. "Hotelling’s T2 in separable Hilbert spaces," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 284-305.
    5. Ramsay, Kelly & Durocher, Stéphane & Leblanc, Alexandre, 2019. "Integrated rank-weighted depth," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 51-69.
    6. 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).

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