Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering

DOI10.1111/biom.12552zbMath1366.62206arXiv1505.04493OpenAlexW2339469603WikidataQ39632696 ScholiaQ39632696MaRDI QIDQ5347400

Wen Zhou, Wen-Xin Zhou, Lan Wang, Jinyuan Chang

Publication date: 23 May 2017

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1505.04493

zbMATH Keywords

hypothesis testingsparsityparametric bootstrapgene clusteringhigh dimensiondifferential expression analysislarge covariance matrices

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bootstrap, jackknife and other resampling methods (62F40) Protein sequences, DNA sequences (92D20)

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Uses Software

• R
• Bioconductor
• CRAN
• gcrma
• HDtest

Cites Work

• Unnamed Item
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• Two sample tests for high-dimensional covariance matrices
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• The control of the false discovery rate in multiple testing under dependency.
• Weak convergence and empirical processes. With applications to statistics
• The asymptotic distribution and Berry-Esseen bound of a new test for independence in high dimension with an application to stochastic optimization
• A two-sample test for high-dimensional data with applications to gene-set testing
• Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
• Bioinformatics and computational biology solutions using R and Bioconductor.
• Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
• Testing differential networks with applications to the detection of gene-gene interactions
• A High-Dimensional Nonparametric Multivariate Test for Mean Vector

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