Testing and support recovery of multiple high-dimensional covariance matrices with false discovery rate control

DOI10.1007/s11749-017-0533-7OpenAlexW2597688463MaRDI QIDQ1694484

Yin Xia

Publication date: 2 February 2018

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11749-017-0533-7

zbMATH Keywords

extreme value distributioncorrectionmultiple testinglimiting null distributionsparsityhigh-dimensional test

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15)

Related Items (1)

Simultaneous Covariance Inference for Multimodal Integrative Analysis

Cites Work

• Unnamed Item
• A test for the equality of covariance matrices when the dimension is large relative to the sample sizes
• Two sample tests for high-dimensional covariance matrices
• Gaussian graphical model estimation with false discovery rate control
• High-dimensional sparse MANOVA
• Shrinkage-based regularization tests for high-dimensional data with application to gene set analysis
• Testing the equality of several covariance matrices with fewer observations than the dimension
• Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
• The control of the false discovery rate in multiple testing under dependency.
• On convergence rates of suprema
• The asymptotic distribution and Berry-Esseen bound of a new test for independence in high dimension with an application to stochastic optimization
• Some high-dimensional tests for a one-way MANOVA
• On false discovery control under dependence
• Large-Scale Multiple Testing under Dependence
• A Direct Estimation Approach to Sparse Linear Discriminant Analysis
• Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size
• Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
• Multiple Testing of Submatrices of a Precision Matrix With Applications to Identification of Between Pathway Interactions
• 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
• False Discovery Control in Large-Scale Spatial Multiple Testing
• Multivariate Theory for Analyzing High Dimensional Data

This page was built for publication: Testing and support recovery of multiple high-dimensional covariance matrices with false discovery rate control