Test for bandedness of high-dimensional covariance matrices and bandwidth estimation

DOI10.1214/12-AOS1002zbMath1257.62064arXiv1208.3321OpenAlexW2128033135MaRDI QIDQ693724

Yumou Qiu, Song Xi Chen

Publication date: 10 December 2012

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1208.3321

zbMATH Keywords

nonparametriclarge \(p\)small \(n\)banded covariance matrixhigh data dimensions

Mathematics Subject Classification ID

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Cites Work

• Unnamed Item
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• Unnamed Item
• High dimensional covariance matrix estimation using a factor model
• Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices
• Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
• Optimal rates of convergence for covariance matrix estimation
• Covariance regularization by thresholding
• Spectral analysis of large dimensional random matrices
• A note on the largest eigenvalue of a large dimensional sample covariance matrix
• On the distribution of the largest eigenvalue in principal components analysis
• Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
• The asymptotic distributions of the largest entries of sample correlation matrices.
• The asymptotic distribution and Berry-Esseen bound of a new test for independence in high dimension with an application to stochastic optimization
• Regularized estimation of large covariance matrices
• Sparse estimation of large covariance matrices via a nested Lasso penalty
• On some test criteria for covariance matrix
• A new approach to Cholesky-based covariance regularization in high dimensions
• Nonparametric estimation of large covariance matrices of longitudinal data
• Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
• Testing for complete independence in high dimensions
• Sparsity and Smoothness Via the Fused Lasso
• Tests for High-Dimensional Covariance Matrices
• Generalized Thresholding of Large Covariance Matrices
• Covariance matrix selection and estimation via penalised normal likelihood
• The distribution of a statistic used for testing sphericity of normal distributions

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