Order Determination for Spiked Type Models

DOI10.5705/ss.202020.0089OpenAlexW3176027794MaRDI QIDQ5089462

Yicheng Zeng, Li Xing Zhu

Publication date: 19 July 2022

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.14498

zbMATH Keywords

phase transitionfactor modelFisher matrixspiked population modelfinite-rank perturbationauto-covariance matrixridge ratio

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Order determination for spiked-type models with a divergent number of spikes

Cites Work

• Fluctuations of the extreme eigenvalues of finite rank deformations of random matrices
• Factor modeling for high-dimensional time series: inference for the number of factors
• Identifying the number of factors from singular values of a large sample auto-covariance matrix
• Extreme eigenvalues of large-dimensional spiked Fisher matrices with application
• The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices
• Anisotropic local laws for random matrices
• Central limit theorems for eigenvalues in a spiked population model
• The largest eigenvalue of small rank perturbations of Hermitian random matrices
• On the distribution of the largest eigenvalue in principal components analysis
• Limiting laws for divergent spiked eigenvalues and largest nonspiked eigenvalue of sample covariance matrices
• Estimation of the number of spikes, possibly equal, in the high-dimensional case
• Universality of covariance matrices
• Eigenvalues of large sample covariance matrices of spiked population models
• ON DETERMINING THE NUMBER OF SPIKES IN A HIGH-DIMENSIONAL SPIKED POPULATION MODEL
• Consistently determining the number of factors in multivariate volatility modelling
• Sufficient dimension reduction through discretization-expectation estimation
• Testing Hypotheses About the Number of Factors in Large Factor Models
• Sliced Inverse Regression for Dimension Reduction
• Dimension Estimation in Noisy PCA With SURE and Random Matrix Theory
• Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator
• Large Sample Covariance Matrices and High-Dimensional Data Analysis
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• On Estimation of the Noise Variance in High Dimensional Probabilistic Principal Component Analysis
• Combining eigenvalues and variation of eigenvectors for order determination
• On Sliced Inverse Regression With High-Dimensional Covariates

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