Selection of the Regularization Parameter in Graphical Models Using Network Characteristics

DOI10.1080/10618600.2017.1366910OpenAlexW2401797185MaRDI QIDQ3391115

Claus-Dieter Mayer, Adria Caballe Mestres, Natalia A. Bochkina

Publication date: 28 March 2022

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

Full work available at URL: https://www.pure.ed.ac.uk/ws/files/42329474/Selection_of_the_Regularization_Parameter_in_Graphical_Models_using_Network_Characteristics.pdf

zbMATH Keywords

clusteringgene expressiongraphical Lassohigh dimensionsparse precision matrixhyperparameter estimation

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• R
• glasso
• huge
• clusfind
• HdBCS

Cites Work

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• A well-conditioned estimator for large-dimensional covariance matrices
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• High-dimensional variable selection
• Clustering rules: A comparison of partitioning and hierarchical clustering algorithms
• Covariance regularization by thresholding
• Wrappers for feature subset selection
• Sparse graphical models for exploring gene expression data
• High-dimensional graphs and variable selection with the Lasso
• Shrinkage Estimators for Covariance Matrices
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• The Structure and Function of Complex Networks
• Stability Selection
• Partial Correlation Estimation by Joint Sparse Regression Models
• The huge Package for High-dimensional Undirected Graph Estimation in R

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