The cluster graphical Lasso for improved estimation of Gaussian graphical models

DOI10.1016/j.csda.2014.11.015OpenAlexW2145354167WikidataQ41495368 ScholiaQ41495368MaRDI QIDQ1623817

Kean Ming Tan, Ali Shojaie, Daniela M. Witten

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1307.5339

zbMATH Keywords

networkhierarchical clusteringsparsitysingle linkage clusteringhigh-dimensional setting

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Probabilistic graphical models (62H22)

Related Items (12)

Hypothesis testing for high-dimensional multivariate regression with false discovery rate control ⋮ A Unified Framework for Structured Graph Learning via Spectral Constraints ⋮ A component lasso ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Estimating large covariance matrix with network topology for high-dimensional biomedical data ⋮ Structured learning of time-varying networks with application to PM_2.5 data ⋮ Incorporating grouping information into Bayesian Gaussian graphical model selection ⋮ Sparse Reduced Rank Huber Regression in High Dimensions ⋮ A note on moment inequality for quadratic forms ⋮ Block-Diagonal Covariance Selection for High-Dimensional Gaussian Graphical Models ⋮ Robust Bayesian model selection for variable clustering with the Gaussian graphical model

Uses Software

• glasso
• ElemStatLearn
• PMA

Cites Work

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• Joint estimation of multiple graphical models
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• Covariance decomposition in undirected Gaussian graphical models
• Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
• Stability Selection
• Partial Correlation Estimation by Joint Sparse Regression Models

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