Model selection for inferring Gaussian graphical models

DOI10.1080/03610918.2021.2007398OpenAlexW3217068723MaRDI QIDQ6204969

Unnamed Author, Daniela de Canditiis

Publication date: 11 April 2024

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

Full work available at URL: https://doi.org/10.1080/03610918.2021.2007398

zbMATH Keywords

model selectionGaussian graphical modelsgrouped Lasso

Mathematics Subject Classification ID

Nonparametric estimation (62G05)

Cites Work

• Unnamed Item
• Penalized methods for bi-level variable selection
• Sparse inverse covariance estimation with the graphical lasso
• Pathwise coordinate optimization for sparse learning: algorithm and theory
• High-dimensional graphs and variable selection with the Lasso
• Model selection and estimation in the Gaussian graphical model
• Estimation of graphical models using the L1,2 norm
• Singular Gaussian graphical models: Structure learning
• Partial Correlation Estimation by Joint Sparse Regression Models
• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

This page was built for publication: Model selection for inferring Gaussian graphical models