A combined \(p\)-value test for the mean difference of high-dimensional data
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Publication:2423870
DOI10.1007/s11425-017-9190-6zbMath1506.62315OpenAlexW2801155266WikidataQ129970150 ScholiaQ129970150MaRDI QIDQ2423870
Wei Yu, Li Xing Zhu, Wang-li Xu
Publication date: 20 June 2019
Published in: Science China. Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11425-017-9190-6
Hypothesis testing in multivariate analysis (62H15) Monte Carlo methods (65C05) Paired and multiple comparisons; multiple testing (62J15) Applications of operator theory in probability theory and statistics (47N30)
Cites Work
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- On multivariate folded normal distribution
- A combined \(p\)-value test for multiple hypothesis testing
- A two-sample test for high-dimensional data with applications to gene-set testing
- An adaptive truncated product method for combining dependent \(p\)-values
- Note on a solution of the generalized Behrens-Fisher problem
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Two-sample behrens-fisher problem for high-dimensional data
- Order Statistics
- Two-Sample Test of High Dimensional Means Under Dependence
- A modified combinedp-value multiple test
- A Two-Sample Test for Equality of Means in High Dimension
- Multivariate Theory for Analyzing High Dimensional Data
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