The beta-mixture shrinkage prior for sparse covariances with near-minimax posterior convergence rate
From MaRDI portal
Publication:2079610
DOI10.1016/j.jmva.2022.105067OpenAlexW4283374231MaRDI QIDQ2079610
Jaeyong Lee, Kyoungjae Lee, Seongil Jo
Publication date: 30 September 2022
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2101.04351
Related Items (3)
Covariance structure estimation with Laplace approximation ⋮ Post-processed posteriors for sparse covariances ⋮ Precision matrix estimation under the horseshoe-like prior-penalty dual
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Regularized linear discriminant analysis and its application in microarrays
- Sparse estimation of a covariance matrix
- Empirical Bayes posterior concentration in sparse high-dimensional linear models
- A well-conditioned estimator for large-dimensional covariance matrices
- Scaling it up: stochastic search structure learning in graphical models
- Posterior convergence rates for estimating large precision matrices using graphical models
- Optimal rates of convergence for sparse covariance matrix estimation
- Improved Stein-type shrinkage estimators for the high-dimensional multivariate normal covariance matrix
- Covariance regularization by thresholding
- Sparsistency and rates of convergence in large covariance matrix estimation
- Nonparametric Stein-type shrinkage covariance matrix estimators in high-dimensional settings
- Variable selection using shrinkage priors
- Hierarchical shrinkage priors for regression models
- Convergence rates of posterior distributions.
- Sparse permutation invariant covariance estimation
- Bayesian structure learning in graphical models
- Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors
- Rate-optimal posterior contraction for sparse PCA
- Adaptive posterior contraction rates for the horseshoe
- Wishart distributions for decomposable covariance graph models
- Posterior contraction in sparse Bayesian factor models for massive covariance matrices
- BAYESIAN HYPER-LASSOS WITH NON-CONVEX PENALIZATION
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- The horseshoe estimator for sparse signals
- The Bayesian Lasso
- Regions in the Complex Plane Containing the Eigenvalues of a Matrix
- Generalized double Pareto shrinkage
- Bayesian Regularization for Graphical Models With Unequal Shrinkage
- Generalized Thresholding of Large Covariance Matrices
- Classification of gene microarrays by penalized logistic regression
- The Graphical Horseshoe Estimator for Inverse Covariance Matrices
This page was built for publication: The beta-mixture shrinkage prior for sparse covariances with near-minimax posterior convergence rate
