High-dimensional correlation matrix estimation for general continuous data with Bagging technique

DOI10.1007/s10994-022-06138-3OpenAlexW4221091040MaRDI QIDQ2102349

Jin Du, Chaojie Wang, Xiaodan Fan

Publication date: 28 November 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-022-06138-3

zbMATH Keywords

random matrixsample correlation matrixpositive-definitenessBagging technique

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• glasso

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bagging predictors
• Estimation of the global minimum variance portfolio in high dimensions
• Sparse inverse covariance estimation with the graphical lasso
• A well-conditioned estimator for large-dimensional covariance matrices
• Retaining positive definiteness in thresholded matrices
• The asymptotic variance matrix of the sample correlation matrix
• Optimal rates of convergence for covariance matrix estimation
• Covariance regularization by thresholding
• Computation of multivariate normal and \(t\) probabilities
• On the distribution of the largest eigenvalue in principal components analysis
• Random matrices: Universality of local eigenvalue statistics up to the edge
• Regularized estimation of large covariance matrices
• Adaptive Thresholding for Sparse Covariance Matrix Estimation
• Improved Estimation of Eigenvalues and Eigenvectors of Covariance Matrices Using Their Sample Estimates
• New Methods for Handling Singular Sample Covariance Matrices
• An overview of the estimation of large covariance and precision matrices
• Generalized Thresholding of Large Covariance Matrices
• A Random Matrix-Theoretic Approach to Handling Singular Covariance Estimates

This page was built for publication: High-dimensional correlation matrix estimation for general continuous data with Bagging technique