Structured learning of time-varying networks with application to PM2.5 data

DOI10.1080/03610918.2019.1582780zbMath1489.62177OpenAlexW2932672582WikidataQ114639913 ScholiaQ114639913MaRDI QIDQ5082613

Xiao Guo, Hai Zhang

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1582780

zbMATH Keywords

ADMMcommunity structuregraphical modeldynamic network\(\mathrm{PM}_{2.5}\)

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of graph theory (05C90) Probabilistic graphical models (62H22)

Uses Software

• glasso
• MIM

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Sparse inverse covariance estimation with the graphical lasso
• Joint estimation of precision matrices in heterogeneous populations
• Estimating time-varying networks
• The cluster graphical Lasso for improved estimation of Gaussian graphical models
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
• Time varying undirected graphs
• Joint estimation of multiple graphical models
• Model selection and estimation in the Gaussian graphical model
• Introduction to Graphical Modelling
• Block-Diagonal Covariance Selection for High-Dimensional Gaussian Graphical Models
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes

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