High-Dimensional Gaussian Graphical Regression Models with Covariates
From MaRDI portal
Publication:6077594
DOI10.1080/01621459.2022.2034632arXiv2011.05245OpenAlexW4210695575MaRDI QIDQ6077594
No author found.
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/2011.05245
sparse group lassoco-expression QTLGaussian graphical model with covariatesnonasymptotic convergence ratesubject-specific Gaussian graphical model
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A sparse conditional Gaussian graphical model for analysis of genetical genomics data
- Sparse inverse covariance estimation with the graphical lasso
- Adjusting for high-dimensional covariates in sparse precision matrix estimation by \(\ell_1\)-penalization
- Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood
- Oracle inequalities and optimal inference under group sparsity
- Some sharp performance bounds for least squares regression with \(L_1\) regularization
- Sparse group Lasso and high dimensional multinomial classification
- On the conditions used to prove oracle results for the Lasso
- Network exploration via the adaptive LASSO and SCAD penalties
- Simultaneous analysis of Lasso and Dantzig selector
- High-dimensional graphs and variable selection with the Lasso
- A Nonparametric Graphical Model for Functional Data With Application to Brain Networks Based on fMRI
- Joint estimation of multiple graphical models
- Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure
- A sparse ising model with covariates
- Model selection and estimation in the Gaussian graphical model
- Power-Law Distributions in Empirical Data
- Tensor SVD: Statistical and Computational Limits
- Group Regularized Estimation Under Structural Hierarchy
- Sparse Estimation of Conditional Graphical Models With Application to Gene Networks
- Model Selection for High-Dimensional Quadratic Regression via Regularization
- Identifying disease‐associated biomarker network features through conditional graphical model
- The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
- Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis
- Bayesian Graphical Regression
- Partial Correlation Estimation by Joint Sparse Regression Models
- Covariate-adjusted precision matrix estimation with an application in genetical genomics
- Model Selection and Estimation in Regression with Grouped Variables
- A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
This page was built for publication: High-Dimensional Gaussian Graphical Regression Models with Covariates