Two-Step Hypothesis Testing When the Number of Variables Exceeds the Sample Size

DOI10.1080/03610918.2012.659819zbMath1347.62090DBLPjournals/cssc/ChiM13OpenAlexW1982729466WikidataQ39796392 ScholiaQ39796392MaRDI QIDQ4921619

Keith E. Muller, Yueh-Yun Chi

Publication date: 13 May 2013

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4028141

zbMATH Keywords

principal component analysisHDLSSMANOVAeigenvalues estimation

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Hypothesis testing in multivariate analysis (62H15)

Cites Work

• Unnamed Item
• Multivariate analysis of variance with fewer observations than the dimension
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• On singular Wishart and singular multivariate beta distributions
• On the distribution of the largest eigenvalue in principal components analysis
• A test for the mean vector with fewer observations than the dimension
• Eigenvalues of large sample covariance matrices of spiked population models
• Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
• Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• Geometric Representation of High Dimension, Low Sample Size Data
• The high-dimension, low-sample-size geometric representation holds under mild conditions

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