The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
From MaRDI portal
Publication:5088225
DOI10.1111/rssb.12033OpenAlexW2165009258WikidataQ39330253 ScholiaQ39330253MaRDI QIDQ5088225
Daniela M. Witten, Patrick Danaher, Pei Wang
Publication date: 11 July 2022
Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1111.0324
high dimensional datagroup lassoGaussian graphical modelnetwork estimationgraphical lassoalternating directions method of multipliersgeneralized fused lasso
Related Items
On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Data, Hypothesis Testing of Matrix Graph Model with Application to Brain Connectivity Analysis, Compressed spectral screening for large-scale differential correlation analysis with application in selecting glioblastoma gene modules, Modeling Massive Highly Multivariate Nonstationary Spatial Data with the Basis Graphical Lasso, Symmetries in directed Gaussian graphical models, Multi-period liability clearing via convex optimal control, Robust Multivariate Lasso Regression with Covariance Estimation, Open issues and recent advances in DC programming and DCA, Prioritizing Autism Risk Genes Using Personalized Graphical Models Estimated From Single-Cell RNA-seq Data, Covariate-adjusted inference for differential analysis of high-dimensional networks, Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer, Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks, Discriminant analysis with Gaussian graphical tree models, Promote sign consistency in the joint estimation of precision matrices, Conditional score matching for high-dimensional partial graphical models, An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using graphics processing units, Exact estimation of multiple directed acyclic graphs, A Unified Framework for Structured Graph Learning via Spectral Constraints, On the robustness of the generalized fused Lasso to prior specifications, Estimating finite mixtures of ordinal graphical models, Bayesian state space models for dynamic genetic network construction across multiple tissues, Discriminant analysis on high dimensional Gaussian copula model, Bayesian joint inference for multiple directed acyclic graphs, Fitting Laplacian regularized stratified Gaussian models, Bayesian inference of clustering and multiple Gaussian graphical models selection, Bayesian graphical models for modern biological applications, Joint Estimation of the Two-Level Gaussian Graphical Models Across Multiple Classes, Estimating Multiple Precision Matrices With Cluster Fusion Regularization, Unnamed Item, Horseshoe shrinkage methods for Bayesian fusion estimation, Unnamed Item, Unnamed Item, Unnamed Item, Joint estimation of multiple Gaussian graphical models across unbalanced classes, Structured learning of time-varying networks with application to PM2.5 data, Estimation of joint directed acyclic graphs with lasso family for gene networks, Fast and Separable Estimation in High-Dimensional Tensor Gaussian Graphical Models, Node-structured integrative Gaussian graphical model guided by pathway information, Differential network inference via the fused D-trace loss with cross variables, Group variable selection via \(\ell_{p,0}\) regularization and application to optimal scoring, Time series graphical Lasso and sparse VAR estimation, Linear Manifold Modeling and Graph Estimation based on Multivariate Functional Data with Different Coarseness Scales, Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease, Learning Gaussian graphical models with latent confounders, A random covariance model for bi‐level graphical modeling with application to resting‐state fMRI data, Testing block‐diagonal covariance structure for high‐dimensional data, Joint estimation of heterogeneous exponential Markov random fields through an approximate likelihood inference, A Bayesian Subset Specific Approach to Joint Selection of Multiple Graphical Models, Robust Shape Matrix Estimation for High-Dimensional Compositional Data with Application to Microbial Inter-Taxa Analysis, Estimation of banded time-varying precision matrix based on SCAD and group Lasso, Joint learning of multiple Granger causal networks via non-convex regularizations: inference of group-level brain connectivity, High-Dimensional Gaussian Graphical Regression Models with Covariates, Transfer Learning in Large-Scale Gaussian Graphical Models with False Discovery Rate Control, Simultaneous Cluster Structure Learning and Estimation of Heterogeneous Graphs for Matrix-Variate fMRI Data, Bayesian Interaction Selection Model for Multimodal Neuroimaging Data Analysis, A Joint Fairness Model with Applications to Risk Predictions for Underrepresented Populations, Joint Gene Network Construction by Single-Cell RNA Sequencing Data, Group-wise shrinkage estimation in penalized model-based clustering, High-dimensional joint estimation of multiple directed Gaussian graphical models, Testing and support recovery of correlation structures for matrix-valued observations with an application to stock market data, Unnamed Item, Chernoff information between Gaussian trees, A constrained \(\ell1\) minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models, A Proximal Point Dual Newton Algorithm for Solving Group Graphical Lasso Problems, Sparse quadratic classification rules via linear dimension reduction, Heterogeneity adjustment with applications to graphical model inference, Gaussian graphical model‐based heterogeneity analysis via penalized fusion, Toward a Multisubject Analysis of Neural Connectivity, Sparse Covariance Matrix Estimation by DCA-Based Algorithms, Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation, Integrative network learning for multimodality biomarker data, Graph informed sliced inverse regression, Unnamed Item, Unnamed Item, Estimation of Graphical Models through Structured Norm Minimization, Simultaneous Clustering and Estimation of Heterogeneous Graphical Models, Regularized joint estimation of related vector autoregressive models, Learning latent variable Gaussian graphical model for biomolecular network with low sample complexity, Unnamed Item, Updating of the Gaussian graphical model through targeted penalized estimation, Block-Diagonal Covariance Selection for High-Dimensional Gaussian Graphical Models, A shrinkage approach to joint estimation of multiple covariance matrices, Efficient local updates for undirected graphical models, Asymptotic properties of concave \(L_1\)-norm group penalties, An efficient algorithm for sparse inverse covariance matrix estimation based on dual formulation, Post-processing posteriors over precision matrices to produce sparse graph estimates, Efficient computation for differential network analysis with applications to quadratic discriminant analysis, Edge selection for undirected graphs, Tuning-Free Heterogeneity Pursuit in Massive Networks, Sparse principal component regression via singular value decomposition approach, Sparse estimation of high-dimensional inverse covariance matrices with explicit eigenvalue constraints, Automatic Response Category Combination in Multinomial Logistic Regression, Estimating Time-Varying Graphical Models, Bayesian Graphical Regression, Functional Graphical Models, Unnamed Item, Reproducible learning in large-scale graphical models, Network differential connectivity analysis, Estimating heterogeneous gene regulatory networks from zero-inflated single-cell expression data, Bayesian Joint Modeling of Multiple Brain Functional Networks, An Efficient Linearly Convergent Regularized Proximal Point Algorithm for Fused Multiple Graphical Lasso Problems, Model-based clustering with sparse covariance matrices, Localizing differentially evolving covariance structures via scan statistics, Fused Multiple Graphical Lasso, Unnamed Item, Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo, Dynamic and robust Bayesian graphical models
