The limits of the sample spiked eigenvalues for a high-dimensional generalized Fisher matrix and its applications
DOI10.1016/j.jspi.2021.03.004zbMath1473.62159arXiv1912.02819OpenAlexW3151803186MaRDI QIDQ2242854
Jiang Hu, Zhi-Qiang Hou, Dandan Jiang
Publication date: 10 November 2021
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.02819
consistent estimatorlimiting spectral distributionalmost sure limitsgeneralized spiked Fisher matrix
Factor analysis and principal components; correspondence analysis (62H25) Characterization and structure theory for multivariate probability distributions; copulas (62H05)
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Cites Work
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- Minimax bounds for sparse PCA with noisy high-dimensional data
- Optimal detection of sparse principal components in high dimension
- CLT for eigenvalue statistics of large-dimensional general Fisher matrices with applications
- Extreme eigenvalues of large-dimensional spiked Fisher matrices with application
- Asymptotics of the principal components estimator of large factor models with weakly influential factors
- Random matrices: universality of local eigenvalue statistics
- Central limit theorems for eigenvalues in a spiked population model
- On sample eigenvalues in a generalized spiked population model
- Finite sample approximation results for principal component analysis: A matrix perturbation approach
- PCA consistency in high dimension, low sample size context
- On the distribution of the largest eigenvalue in principal components analysis
- Asymptotics of empirical eigenstructure for high dimensional spiked covariance
- Testing in high-dimensional spiked models
- Limiting laws for divergent spiked eigenvalues and largest nonspiked eigenvalue of sample covariance matrices
- Generalized four moment theorem and an application to CLT for spiked eigenvalues of high-dimensional covariance matrices
- Testing for independence of large dimensional vectors
- Eigenvalues of large sample covariance matrices of spiked population models
- Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
- Testing Hypotheses About the Number of Factors in Large Factor Models
- Roy’s largest root test under rank-one alternatives
- Determining the Number of Factors in Approximate Factor Models
- Distributions of Matrix Variates and Latent Roots Derived from Normal Samples
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