Shrinking the Covariance Matrix Using Convex Penalties on the Matrix-Log Transformation

DOI10.1080/10618600.2020.1814788OpenAlexW3082179244MaRDI QIDQ5066396

Mengxi Yi, David E. Tyler

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2020.1814788

zbMATH Keywords

Newton-Raphson algorithmgeodesic convexitypenalized likelihood

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• glasso

Cites Work

• Sparse inverse covariance estimation with the graphical lasso
• A canonical definition of shape
• All convex invariant functions of hermitian matrices
• Optimal rates of convergence for covariance matrix estimation
• Sparsistency and rates of convergence in large covariance matrix estimation
• Majorization, doubly stochastic matrices, and comparison of eigenvalues
• High dimensional covariance matrix estimation by penalizing the matrix-logarithm transformed likelihood
• Regularized estimation of large covariance matrices
• Model selection and estimation in the Gaussian graphical model
• Unified Framework to Regularized Covariance Estimation in Scaled Gaussian Models
• Multivariate Generalized Gaussian Distribution: Convexity and Graphical Models
• Iterative Solution of Nonlinear Equations in Several Variables
• Lassoing eigenvalues
• Conic Geometric Optimization on the Manifold of Positive Definite Matrices
• A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices
• Structured Robust Covariance Estimation
• Unnamed Item

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