High dimensional sparse covariance estimation via directed acyclic graphs

DOI10.1214/09-EJS534zbMath1326.62124arXiv0911.2375OpenAlexW3105320479MaRDI QIDQ1952020

Philipp Rütimann, Peter Bühlmann

Publication date: 27 May 2013

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

Full work available at URL: https://arxiv.org/abs/0911.2375

zbMATH Keywords

covariance matrixhigh-dimensional datadirected acyclic graphsgraphical LassoPC-algorithmconcentration matrix

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Graphical methods in statistics (62A09)

Related Items (11)

Covariance structure approximation via gLasso in high-dimensional supervised classification ⋮ Estimating Large Precision Matrices via Modified Cholesky Decomposition ⋮ A permutation-based Bayesian approach for inverse covariance estimation ⋮ Bayesian inference of causal effects from observational data in Gaussian graphical models ⋮ Scalable Bayesian high-dimensional local dependence learning ⋮ Consistent Bayesian sparsity selection for high-dimensional Gaussian DAG models with multiplicative and beta-mixture priors ⋮ Remodeling and estimation for sparse partially linear regression models ⋮ A general framework for Vecchia approximations of Gaussian processes ⋮ Posterior graph selection and estimation consistency for high-dimensional Bayesian DAG models ⋮ Cross-Dimensional Inference of Dependent High-Dimensional Data ⋮ Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors

Uses Software

• ggm
• pcalg
• glasso
• TETRAD

Cites Work

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