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Science China Mathematics

, Volume 62, Issue 5, pp 961–978 | Cite as

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

  • Wei Yu
  • Wangli Xu
  • Lixing ZhuEmail author
Articles
  • 57 Downloads

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.

Keywords

high-dimensional data equality of means multiple hypothesis testing sparse alternatives 

MSC(2010)

47N30 65C05 

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Notes

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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Copyright information

© Science China Press and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Center for Applied Statistics, School of StatisticsRenmin University of ChinaBeijingChina
  2. 2.School of StatisticsBeijing Normal UniversityBeijingChina
  3. 3.Department of MathematicsHong Kong Baptist UniversityHong KongChina

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