Positive-Definite ℓ1-Penalized Estimation of Large Covariance Matrices
From MaRDI portal
Publication:4904725
DOI10.1080/01621459.2012.725386zbMath1258.62063arXiv1208.5702OpenAlexW2113968881MaRDI QIDQ4904725
Hui Zou, Lingzhou Xue, Shi-Qian Ma
Publication date: 31 January 2013
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1208.5702
Related Items
Cholesky-based model averaging for covariance matrix estimation, Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas, A Cholesky-based estimation for large-dimensional covariance matrices, A sequential test for variable selection in high dimensional complex data, Ensemble sparse estimation of covariance structure for exploring genetic disease data, Regularization for high-dimensional covariance matrix, Gaussian Patch Mixture Model Guided Low-Rank Covariance Matrix Minimization for Image Denoising, A dual active-set proximal Newton algorithm for sparse approximation of correlation matrices, High dimensional covariance matrix estimation by penalizing the matrix-logarithm transformed likelihood, Sparse estimation of high-dimensional correlation matrices, Alternating proximal gradient method for convex minimization, A graphical model selection tool for mixed models, On the penalized maximum likelihood estimation of high-dimensional approximate factor model, D-trace estimation of a precision matrix using adaptive lasso penalties, Fixed support positive-definite modification of covariance matrix estimators via linear shrinkage, A Compound Decision Approach to Covariance Matrix Estimation, On variable ordination of Cholesky‐based estimation for a sparse covariance matrix, Robust Shape Matrix Estimation for High-Dimensional Compositional Data with Application to Microbial Inter-Taxa Analysis, High-dimensional Markowitz portfolio optimization problem: empirical comparison of covariance matrix estimators, Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding, Efficient estimation of approximate factor models via penalized maximum likelihood, Power enhancement for testing multi-factor asset pricing models via Fisher's method, Positive-definite thresholding estimators of covariance matrices with zeros, Sharp optimality for high-dimensional covariance testing under sparse signals, Semi-parametric inference for large-scale data with temporally dependent noise, Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis, Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection, Sparse Markowitz portfolio selection by using stochastic linear complementarity approach, Sparse Covariance Matrix Estimation by DCA-Based Algorithms, An implementable first-order primal-dual algorithm for structured convex optimization, A multiple testing approach to the regularisation of large sample correlation matrices, An efficient numerical method for condition number constrained covariance matrix approximation, Covariance estimation via sparse Kronecker structures, Estimation of Graphical Models through Structured Norm Minimization, Diagonally Dominant Principal Component Analysis, Weighted covariance matrix estimation, Estimation and optimal structure selection of high-dimensional Toeplitz covariance matrix, Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models, Sparse and low-rank covariance matrix estimation, Double shrinkage estimators for large sparse covariance matrices, Tuning-parameter selection in regularized estimations of large covariance matrices, Some Statistical Problems with High Dimensional Financial data, Graph-Guided Banding of the Covariance Matrix, Positive-definite modification of a covariance matrix by minimizing the matrix \(\ell_{\infty}\) norm with applications to portfolio optimization, Fourier transform sparse inverse regression estimators for sufficient variable selection, Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension, Correlation structure selection for longitudinal data with diverging cluster size, High-performance statistical computing in the computing environments of the 2020s, Estimation of a sparse and spiked covariance matrix, Conditioning theory of the equality constrained quadratic programming and its applications, An improved banded estimation for large covariance matrix
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Sparse inverse covariance estimation with the graphical lasso
- The Adaptive Lasso and Its Oracle Properties
- High-dimensionality effects in the Markowitz problem and other quadratic programs with linear constraints: risk underestimation
- Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter vari\-ants
- Are a set of microarrays independent of each other?
- Optimal rates of convergence for covariance matrix estimation
- Covariance regularization by thresholding
- Operator norm consistent estimation of large-dimensional sparse covariance matrices
- On the distribution of the largest eigenvalue in principal components analysis
- Regularized estimation of large covariance matrices
- Nonparametric estimation of large covariance matrices of longitudinal data
- Positive definite estimators of large covariance matrices
- Adaptive First-Order Methods for General Sparse Inverse Covariance Selection
- Sparse and stable Markowitz portfolios
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms
- The Numerical Solution of Parabolic and Elliptic Differential Equations
- First-Order Methods for Sparse Covariance Selection
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Vast Portfolio Selection With Gross-Exposure Constraints
- Generalized Thresholding of Large Covariance Matrices
- DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
