Precision matrix estimation under the horseshoe-like prior-penalty dual

DOI10.1214/23-ejs2196arXiv2104.10750OpenAlexW3153760897WikidataQ129454896 ScholiaQ129454896MaRDI QIDQ6200870

Ksheera Sagar, Sayantan Banerjee, Jyotishka Datta, Anindya Bhadra

Publication date: 25 March 2024

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2104.10750

zbMATH Keywords

non-convex optimizationgraphical modelssparsityposterior consistencyposterior concentration

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Bayesian inference (62F15)

Cites Work

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• Sparsity information and regularization in the horseshoe and other shrinkage priors
• Sparse inverse covariance estimation with the graphical lasso
• Bayesian structure learning in sparse Gaussian graphical models
• Scaling it up: stochastic search structure learning in graphical models
• Optimal shrinkage estimation of mean parameters in family of distributions with quadratic variance
• Covariance estimation: the GLM and regularization perspectives
• Posterior convergence rates for estimating large precision matrices using graphical models
• Bayesian linear regression with sparse priors
• Coordinate descent algorithm for covariance graphical Lasso
• High dimensional posterior convergence rates for decomposable graphical models
• One-step sparse estimates in nonconcave penalized likelihood models
• Sparsistency and rates of convergence in large covariance matrix estimation
• Ridge estimation of inverse covariance matrices from high-dimensional data
• Uncertainty quantification for the horseshoe (with discussion)
• Convergence rates of posterior distributions.
• Sparse permutation invariant covariance estimation
• Bayesian structure learning in graphical models
• The beta-mixture shrinkage prior for sparse covariances with near-minimax posterior convergence rate
• Network exploration via the adaptive LASSO and SCAD penalties
• Lasso meets horseshoe: a survey
• Regularized estimation of large covariance matrices
• Bayesian graphical Lasso models and efficient posterior computation
• Shrinkage estimation of large dimensional precision matrix using random matrix theory
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Maximum likelihood estimation via the ECM algorithm: A general framework
• The horseshoe estimator for sparse signals
• The Bayesian Lasso
• Normal Variance-Mean Mixtures and z Distributions
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Regions in the Complex Plane Containing the Eigenvalues of a Matrix
• Cholesky decomposition of a hyper inverse Wishart matrix
• Estimating Large Precision Matrices via Modified Cholesky Decomposition
• The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
• An overview of the estimation of large covariance and precision matrices
• Bayesian Regularization for Graphical Models With Unequal Shrinkage
• The Power of Convex Relaxation: Near-Optimal Matrix Completion
• Regularization and Variable Selection Via the Elastic Net
• Dirichlet–Laplace Priors for Optimal Shrinkage
• Default Bayesian analysis with global-local shrinkage priors
• Sparse precision matrix estimation via lasso penalized D-trace loss
• The Graphical Horseshoe Estimator for Inverse Covariance Matrices

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