Learning Gaussian graphical models with latent confounders

DOI10.1016/j.jmva.2023.105213arXiv2105.06600OpenAlexW3160459994MaRDI QIDQ6051077

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Publication date: 19 September 2023

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.06600

zbMATH Keywords

principal component analysislatent variableshigh-dimensional dataGaussian graphical models

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Multivariate analysis (62Hxx) Probabilistic graphical models (62H22)

Cites Work

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• Nearly unbiased variable selection under minimax concave penalty
• Sparse inverse covariance estimation with the graphical lasso
• Latent variable graphical model selection via convex optimization
• Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
• Concentration inequalities and moment bounds for sample covariance operators
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• Covariance regularization by thresholding
• Sparsistency and rates of convergence in large covariance matrix estimation
• Principal component analysis.
• Sparse permutation invariant covariance estimation
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
• A rationale and test for the number of factors in factor analysis
• Regularized estimation of large covariance matrices
• High-dimensional graphs and variable selection with the Lasso
• Exact matrix completion via convex optimization
• Latent Variable Models and Factor Analysis
• Statistical mechanics of complex networks
• Rank-Sparsity Incoherence for Matrix Decomposition
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Model selection and estimation in the Gaussian graphical model
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation
• High-Dimensional Statistics
• Stability Selection
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Partial Correlation Estimation by Joint Sparse Regression Models
• A Convex Pseudolikelihood Framework for High Dimensional Partial Correlation Estimation with Convergence Guarantees
• Learning Latent Tree Graphical Models
• The huge Package for High-dimensional Undirected Graph Estimation in R
• Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation
• Discussion: Latent variable graphical model selection via convex optimization

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