Joint estimation of multiple Gaussian graphical models across unbalanced classes

DOI10.1016/j.csda.2017.11.009zbMath1469.62140OpenAlexW2778405867MaRDI QIDQ1662174

Inyoung Kim, Liang Shan

Publication date: 17 August 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2017.11.009

zbMATH Keywords

precision matrix estimationgene network explorationjoint adaptive graphical Lassounbalanced multi-class

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Probabilistic graphical models (62H22)

Related Items (2)

Joint Estimation of the Two-Level Gaussian Graphical Models Across Multiple Classes ⋮ A joint estimation for the high-dimensional regression modeling on stratified data

Uses Software

• glasso

Cites Work

• Unnamed Item
• Sparse inverse covariance estimation with the graphical lasso
• The Adaptive Lasso and Its Oracle Properties
• Sparse permutation invariant covariance estimation
• Network exploration via the adaptive LASSO and SCAD penalties
• Regularized estimation of large covariance matrices
• High-dimensional graphs and variable selection with the Lasso
• Emergence of Scaling in Random Networks
• Joint estimation of multiple graphical models
• Model selection and estimation in the Gaussian graphical model
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Regression analysis of networked data

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