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A combined p-value test for the mean difference of high-dimensional data

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Abstract

This paper proposes a novel method for testing the equality of high-dimensional means using a multiple hypothesis test. The proposed method is based on the maximum of standardized partial sums of logarithmic p-values statistic. Numerical studies show that the method performs well for both normal and non-normal data and has a good power performance under both dense and sparse alternative hypotheses. For illustration, a real data analysis is implemented.

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Acknowledgments

This work was supported by a grant from the University Grants Council of Hong Kong, National Natural Science Foundation of China (Grant No. 11471335), the Ministry of Education project of Key Research Institute of Humanities and Social Sciences at Universities (Grant No. 16JJD910002), and Fund for Building World-Class Universities (Disciplines) of Renmin University of China. The authors thank two referees for their constructive comments that led to an improvement of an early version of the article.

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Authors and Affiliations

  1. Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, 100872, China

    Wei Yu & Wangli Xu

  2. School of Statistics, Beijing Normal University, Beijing, 100872, China

    Lixing Zhu

  3. Department of Mathematics, Hong Kong Baptist University, Hong Kong, China

    Lixing Zhu

Authors
  1. Wei Yu
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  2. Wangli Xu
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  3. Lixing Zhu
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Correspondence to Lixing Zhu.

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Yu, W., Xu, W. & Zhu, L. A combined p-value test for the mean difference of high-dimensional data. Sci. China Math. 62, 961–978 (2019). https://doi.org/10.1007/s11425-017-9190-6

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  • DOI: https://doi.org/10.1007/s11425-017-9190-6

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