Transfer Learning in Large-Scale Gaussian Graphical Models with False Discovery Rate Control

DOI10.1080/01621459.2022.2044333arXiv2010.11037OpenAlexW3094555844WikidataQ111520957 ScholiaQ111520957MaRDI QIDQ6077601

Unnamed Author, Hongzhe Li, Sai Li

Publication date: 18 October 2023

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

Full work available at URL: https://arxiv.org/abs/2010.11037

zbMATH Keywords

multiple testingmeta learninginverse covariance matrixdebiased estimator

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Transfer learning for contextual multi-armed bandits, Transfer Learning under High-dimensional Generalized Linear Models

Cites Work

• Unnamed Item
• Sparse inverse covariance estimation with the graphical lasso
• Asymptotic normality and optimalities in estimation of large Gaussian graphical models
• Gaussian graphical model estimation with false discovery rate control
• Estimating sparse precision matrix: optimal rates of convergence and adaptive estimation
• Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
• TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models
• Exponential screening and optimal rates of sparse estimation
• Inferring multiple graphical structures
• Sparsistency and rates of convergence in large covariance matrix estimation
• Sparse permutation invariant covariance estimation
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
• A no-free-lunch theorem for multitask learning
• Optimal learning with \textit{Q}-aggregation
• Transfer learning for nonparametric classification: minimax rate and adaptive classifier
• Joint estimation of multiple high-dimensional precision matrices
• Direct estimation of differential networks
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Joint estimation of multiple graphical models
• Model selection and estimation in the Gaussian graphical model
• Semisupervised Inference for Explained Variance in High Dimensional Linear Regression and its Applications
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Testing differential networks with applications to the detection of gene-gene interactions
• Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation