Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach

DOI10.1080/01621459.2020.1841645zbMath1464.62462OpenAlexW3095267404MaRDI QIDQ4999152

Junwei Lu, Cong Ma, Han Liu

Publication date: 6 July 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2020.1841645

zbMATH Keywords

nuisance parameterGaussian graphical modelssample splittinguncertainty assessmentfMRI data

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Probabilistic graphical models (62H22)

Uses Software

• glasso

Cites Work

• Unnamed Item
• Unnamed Item
• On asymptotically optimal confidence regions and tests for high-dimensional models
• Confidence intervals for high-dimensional inverse covariance estimation
• Sparse inverse covariance estimation with the graphical lasso
• Estimating sparse precision matrix: optimal rates of convergence and adaptive estimation
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models
• The statistical analysis of fMRI data
• Sparse permutation invariant covariance estimation
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
• Regularized estimation of large covariance matrices
• High-dimensional graphs and variable selection with the Lasso
• Direct estimation of differential networks
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Partial Gaussian Graphical Model Estimation
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Model selection and estimation in the Gaussian graphical model
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

This page was built for publication: Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach