Discussion: Latent variable graphical model selection via convex optimization (Q5970644)

scientific article; zbMATH DE number 6142906

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English	Discussion: Latent variable graphical model selection via convex optimization	scientific article; zbMATH DE number 6142906

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instance of

scholarly article

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title

Discussion: Latent variable graphical model selection via convex optimization (English)

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The Annals of Statistics

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publication date

7 March 2013

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full work available at URL

https://arxiv.org/abs/1211.0806

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https://projecteuclid.org/euclid.aos/1351602529

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describes a project that uses

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MaRDI publication profile

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cites work

Regularized estimation of large covariance matrices

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Covariance regularization by thresholding

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Robust principal component analysis?

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Exact matrix completion via convex optimization

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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

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Rejoinder: Latent variable graphical model selection via convex optimization

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Rank-Sparsity Incoherence for Matrix Decomposition

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Q2760174

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For most large underdetermined systems of linear equations the minimal 𝓁<sub>1</sub>‐norm solution is also the sparsest solution

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Compressed sensing

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Operator norm consistent estimation of large-dimensional sparse covariance matrices

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High dimensional covariance matrix estimation using a factor model

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On the distribution of the largest eigenvalue in principal components analysis

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Sparsistency and rates of convergence in large covariance matrix estimation

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A well-conditioned estimator for large-dimensional covariance matrices

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Q5531480

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High-dimensional graphs and variable selection with the Lasso

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Q5652137

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High-dimensional covariance estimation by minimizing $\ell _{1}$-penalized log-determinant divergence

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Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization

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Equivalent Subgradient Versions of Hamiltonian and Euler–Lagrange Equations in Variational Analysis

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Sparse permutation invariant covariance estimation

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Gaussian Markov distributions over finite graphs

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SDPT3 — A Matlab software package for semidefinite programming, Version 1.3

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Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)

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Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm

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Characterization of the subdifferential of some matrix norms

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Nonparametric estimation of large covariance matrices of longitudinal data

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Q3174050

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Identifiers

zbMATH Open document ID

1288.62089

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DOI

10.1214/12-AOS980

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Mathematics Subject Classification ID

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Mathematics(1 entry)

mardi Publication:5970644