Estimating heterogeneous graphical models for discrete data with an application to roll call voting

DOI10.1214/13-AOAS700zbMath1397.62195arXiv1509.04828WikidataQ31096248 ScholiaQ31096248MaRDI QIDQ746672

Elizaveta Levina, Jian Guo, Jie Cheng, George Michailidis, Ji Zhu

Publication date: 28 October 2015

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1509.04828

zbMATH Keywords

parameter estimationhigh-dimensional datagraphical modelsbinary dataMarkov network\(\ell_{1}\) penaltygroup penalty

Mathematics Subject Classification ID

Multivariate analysis (62H99) Estimation in multivariate analysis (62H12) Applications of statistics to social sciences (62P25) Applications of graph theory (05C90)

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Cites Work

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• Sparse estimators and the oracle property, or the return of Hodges' estimator
• Group variable selection via a hierarchical lasso and its oracle property
• High-dimensional structure estimation in Ising models: local separation criterion
• Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
• On the distribution of penalized maximum likelihood estimators: the LASSO, SCAD, and thresholding
• Learning a common substructure of multiple graphical Gaussian models
• Horseshoes in multidimensional scaling and local kernel methods
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• Sparse permutation invariant covariance estimation
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
• High-dimensional graphs and variable selection with the Lasso
• Emergence of Scaling in Random Networks
• Joint estimation of multiple graphical models
• Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks
• Model selection and estimation in the Gaussian graphical model
• Graphical Models, Exponential Families, and Variational Inference
• Statistical Inference in Context Specific Interaction Models for Contingency Tables
• Partial Correlation Estimation by Joint Sparse Regression Models
• Fused Multiple Graphical Lasso
• Regularization and Variable Selection Via the Elastic Net