Testing for subsphericity when \(n\) and \(p\) are of different asymptotic order

DOI10.1016/j.spl.2021.109209zbMath1478.62138arXiv2101.09711OpenAlexW3191565991MaRDI QIDQ2244531

Joni Virta

Publication date: 12 November 2021

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.09711

zbMATH Keywords

Wishart distributionsample covariance matrixPCAhigh-dimensional statisticsdimension estimation

Mathematics Subject Classification ID

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

Related Items (1)

On testing the equality of latent roots of scatter matrices under ellipticity

Cites Work

• PCA consistency in high dimension, low sample size context
• Bootstrap tests and confidence regions for functions of a covariance matrix
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• Principal component analysis.
• A high-dimensional test for the equality of the smallest eigenvalues of a covariance matrix
• Eigenvalues of large sample covariance matrices of spiked population models
• A note on testing the covariance matrix for large dimension
• A General Framework For Consistency of Principal Component Analysis
• PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context
• A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise
• Large Sample Covariance Matrices and High-Dimensional Data Analysis
• Combining eigenvalues and variation of eigenvectors for order determination

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