PCA and eigen-inference for a spiked covariance model with largest eigenvalues of same asymptotic order

DOI10.1214/12-BJPS205zbMath1319.62118OpenAlexW1995056847MaRDI QIDQ398204

Addy Bolivar-Cime, Victor Perez-Abreu

Publication date: 12 August 2014

Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bjps/1396615440

zbMATH Keywords

principal component analysishypothesis testconfidence intervalhigh dimensional dataHDLSSspiked covariance modeleigeninference

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Asymptotic distribution theory in statistics (62E20)

Related Items (1)

A classifier under the strongly spiked eigenvalue model in high-dimension, low-sample-size context

Cites Work

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• Boundary behavior in high dimension, low sample size asymptotics of PCA
• Central limit theorems for eigenvalues in a spiked population model
• PCA consistency in high dimension, low sample size context
• On the distribution of the largest eigenvalue in principal components analysis
• Eigenvalues of large sample covariance matrices of spiked population models
• The distribution of the sphericity test criterion
• PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context
• Geometric Representation of High Dimension, Low Sample Size Data
• The high-dimension, low-sample-size geometric representation holds under mild conditions
• On the Exact Distributions of the Criterion $W$ for Testing Sphericity in a $p$-Variate Normal Distribution
• On the Exact Distributions of Votaw's Criteria for Testing Compound Symmetry of a Covariance Matrix

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