ESTIMATING HETEROGENEOUS GRAPHICAL MODELS FOR DISCRETE DATA WITH AN APPLICATION TO ROLL CALL VOTING
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Summary
A Markov graphical model is developed via a joint estimation method that preserves the underlying common graph structure, but also allows for differences between the networks, and employs a group penalty that targets the common zero interaction effects across all the networks.
- Type
- article
- Published
- 2015-06-01
- Cited by
- 33
- References
- 47
- Access
- Open access
- OpenAlex
- https://openalex.org/W27182289
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1959229
Keywords
Computer science
References
- Consistent neighbourhood selection for sparse high-dimensional graphs with the Lasso
- On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding
- Improved Estimation of High-dimensional Ising Models
- Fused Multiple Graphical Lasso
- On the Statistical Analysis of Dirty Pictures
- Markov Random Field Modeling in Image Analysis
- PRINCIPAL COMPONENT ANALYSIS OF SENATE VOTING PATTERNS
- Markov random field based English Part-Of-Speech tagging system
- Analysing Roll Calls of the European Parliament
- HORSESHOES IN MULTIDIMENSIONAL SCALING AND LOCAL KERNEL METHODS
- Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
- High-dimensional graphs and variable selection with the Lasso
- Sparse estimators and the oracle property, or the return of Hodges’ estimator
- The Spatial Theory of Voting: An Introduction
- High-dimensional structure estimation in Ising models: Local separation criterion
- Statistical Inference in Context Specific Interaction Models for Contingency Tables
- Model selection and estimation in the Gaussian graphical model
- Learning a common substructure of multiple graphical Gaussian models
- High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence
- Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining
Cited by
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- Measuring and accounting for strategic abstentions in the US Senate, 1989–2012
- Hierarchical Graphical Models, With Application to Systemic Risk
- Bayesian graphical models with economic and financial applications
- Replicates in high dimensions, with applications to latent variable graphical models
- Structure estimation of binary graphical models on stratified data: Application to the description of injury tables for victims of road accidents
- Mixed and Covariate Dependent Graphical Models.
- Estimating Social Opinion Dynamics Models From Voting Records
- Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks
- Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data
- Sample Complexity of Joint Structure Learning
- Bayesian Analysis of High-dimensional Discrete Graphical Models
- Heterogeneity adjustment with applications to graphical model inference
- Structure Learning with Side Information: Sample Complexity
- Structure Learning of Similar Ising Models: Information-theoretic Bounds
- On Ising models and algorithms for the construction of symptom networks in psychopathological research.
- Approximate Recovery Of Ising Models with Side Information
- Probabilistic Decision Modeling in Social Networks
- Dependence Graphs Based on Association Rules to Explore Delusional Experiences
- Sampling Algorithms for Discrete Markov Random Fields and Related Graphical Models
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