Principal regression for high dimensional covariance matrices
From MaRDI portal
Publication:2233571
DOI10.1214/21-EJS1887zbMath1471.62433arXiv2007.12740OpenAlexW3200776430MaRDI QIDQ2233571
Publication date: 11 October 2021
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2007.12740
Multivariate analysis (62H99) Applications of statistics to biology and medical sciences; meta analysis (62P10) Linear inference, regression (62J99)
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- A well-conditioned estimator for large-dimensional covariance matrices
- Nonlinear shrinkage estimation of large-dimensional covariance matrices
- Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
- Simultaneous modelling of the Cholesky decomposition of several covariance matrices
- The statistical analysis of fMRI data
- On the limit of the largest eigenvalue of the large dimensional sample covariance matrix
- A distribution-free M-estimator of multivariate scatter
- Pseudo maximum likelihood estimation: Theory and applications
- Asymptotically efficient estimation of covariance matrices with linear structure
- Sparse principal component based high-dimensional mediation analysis
- A Covariance Regression Model
- Shrinkage Estimators for Covariance Matrices
- The Matrix-Logarithmic Covariance Model
- Spectral models for covariance matrices
- Robust Shrinkage Estimation of High-Dimensional Covariance Matrices
- Generalized Robust Shrinkage Estimator and Its Application to STAP Detection Problem
- A Hierarchical Eigenmodel for Pooled Covariance Estimation
- Sparsity and Smoothness Via the Fused Lasso
- Rejoinder
- Asymptotic Theory for Principal Component Analysis
- Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation
