D-trace estimation of a precision matrix using adaptive lasso penalties

DOI10.1007/s11634-016-0272-8zbMath1414.62224OpenAlexW2516747027MaRDI QIDQ2418368

Vahe Avagyan, Francisco J. Nogales, Andrés M. Alonso

Publication date: 3 June 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10016/32938

zbMATH Keywords

gene expression datahigh-dimensionalityGaussian graphical modeladaptive thresholdingD-trace loss

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Analysis of variance and covariance (ANOVA) (62J10) Graphical methods in numerical analysis (65S05)

Related Items (4)

D-Trace estimation of a precision matrix with eigenvalue control ⋮ Precision matrix estimation under data contamination with an application to minimum variance portfolio selection ⋮ Precision matrix estimation using penalized generalized Sylvester matrix equation ⋮ Regularized estimation of precision matrix for high-dimensional multivariate longitudinal data

Uses Software

• glasso

Cites Work

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• Sparse inverse covariance estimation with the graphical lasso
• The Adaptive Lasso and Its Oracle Properties
• A well-conditioned estimator for large-dimensional covariance matrices
• Nonlinear shrinkage estimation of large-dimensional covariance matrices
• Adjusting for high-dimensional covariates in sparse precision matrix estimation by \(\ell_1\)-penalization
• Dominating estimators for minimum-variance portfolios
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Operator norm consistent estimation of large-dimensional sparse covariance matrices
• Relaxed Lasso
• Sparsistency and rates of convergence in large covariance matrix estimation
• Empirical Bayes estimation of the multivariate normal covariance matrix
• A joint convex penalty for inverse covariance matrix estimation
• Nonparametric Stein-type shrinkage covariance matrix estimators in high-dimensional settings
• Sparse estimation of high-dimensional correlation matrices
• On the distribution of the largest eigenvalue in principal components analysis
• Adaptive covariance matrix estimation through block thresholding
• Sparse permutation invariant covariance estimation
• The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
• Bayesian structure learning in graphical models
• Computationally efficient banding of large covariance matrices for ordered data and connections to banding the inverse Cholesky factor
• Network exploration via the adaptive LASSO and SCAD penalties
• Outlier detection and robust covariance estimation using mathematical programming
• Regularized estimation of large covariance matrices
• High-dimensional graphs and variable selection with the Lasso
• A Gaussian graphical model approach to climate networks
• Positive definite estimators of large covariance matrices
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Model selection and estimation in the Gaussian graphical model
• First-Order Methods for Sparse Covariance Selection
• Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Positive-Definite ℓ₁-Penalized Estimation of Large Covariance Matrices
• Generalized Thresholding of Large Covariance Matrices
• Sparse precision matrix estimation via lasso penalized D-trace loss

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