The performance of covariance selection methods that consider decomposable models only

DOI10.1214/14-BA874zbMath1327.62389OpenAlexW1988399610MaRDI QIDQ899046

M. Beatrix Jones, Hélène Massam, A. Marie Fitch

Publication date: 21 December 2015

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1409921109

zbMATH Keywords

asymptotic behaviornon-decomposablecovariance selectiongraphical lassodecomposablefeature inclusion stochastic searchundirected Gaussian graphical models

Mathematics Subject Classification ID

Multivariate analysis (62H99) Asymptotic distribution theory in statistics (62E20) Bayesian inference (62F15)

Related Items (7)

The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models ⋮ Objective methods for graphical structural learning ⋮ Bayesian graph selection consistency under model misspecification ⋮ Efficient Bayesian regularization for graphical model selection ⋮ Learning Gaussian graphical models with fractional marginal pseudo-likelihood ⋮ Post-processing posteriors over precision matrices to produce sparse graph estimates ⋮ Structural learning of contemporaneous dependencies in graphical VAR models

Uses Software

• R
• glasso
• HdBCS

Cites Work

• Sparse inverse covariance estimation with the graphical lasso
• Network exploration via the adaptive LASSO and SCAD penalties
• Experiments in stochastic computation for high-dimensional graphical models
• High-dimensional graphs and variable selection with the Lasso
• Efficient estimation of covariance selection models
• Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data
• Objective Bayesian model selection in Gaussian graphical models
• Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: The performance of covariance selection methods that consider decomposable models only