Assessment of Covariance Selection Methods in High-Dimensional Gaussian Graphical Models

DOI10.5540/tcam.2022.023.03.00583zbMath1525.62023MaRDI QIDQ6066549

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Publication date: 13 December 2023

Published in: Trends in Computational and Applied Mathematics (Search for Journal in Brave)

zbMATH Keywords

glassoGaussian graphical modelcovariance selection

Mathematics Subject Classification ID

Probabilistic graphical models (62H22)

Cites Work

• A sparse conditional Gaussian graphical model for analysis of genetical genomics data
• Sparse inverse covariance estimation with the graphical lasso
• A well-conditioned estimator for large-dimensional covariance matrices
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• High-dimensional graphs and variable selection with the Lasso
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks
• Introduction to Graphical Modelling
• High‐Dimensional Covariance Estimation
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