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

Wei Yu; Wangli Xu; Lixing ZHU

doi:10.1007/s11425-017-9190-6

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

Wei Yu, Wangli Xu, Lixing ZHU^*

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Journal article › peer-review

2 Citations (Scopus)

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.

Original language	English
Pages (from-to)	961-978
Number of pages	18
Journal	Science China Mathematics
Volume	62
Issue number	5
DOIs	https://doi.org/10.1007/s11425-017-9190-6
Publication status	Published - 1 May 2019

Scopus Subject Areas

General Mathematics

User-Defined Keywords

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

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10.1007/s11425-017-9190-6

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title = "A combined p-value test for the mean difference of high-dimensional data",

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

author = "Wei Yu and Wangli Xu and Lixing ZHU",

note = "Funding Information: 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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T1 - A combined p-value test for the mean difference of high-dimensional data

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AU - Xu, Wangli

AU - ZHU, Lixing

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PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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.

AB - 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.

KW - equality of means

KW - high-dimensional data

KW - multiple hypothesis testing

KW - sparse alternatives

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

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