Representing Sparse Gaussian DAGs as Sparse R-Vines Allowing for Non-Gaussian Dependence
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Publication:3391116
DOI10.1080/10618600.2017.1366911OpenAlexW2964300389MaRDI QIDQ3391116
Publication date: 28 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1604.04202
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Regular vines with strongly chordal pattern of (conditional) independence, Dependence modelling in ultra high dimensions with vine copulas and the graphical Lasso, Selection of sparse vine copulas in high dimensions with the Lasso
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- Pair-copula constructions of multiple dependence
- Selecting and estimating regular vine copulae and application to financial returns
- Sequential Bayesian model selection of regular vine copulas
- Simplified pair copula constructions -- limitations and extensions
- Copula directed acyclic graphs
- Normal linear regression models with recursive graphical Markov structure
- Parsimonious parameterization of correlation matrices using truncated vines and factor analysis
- Structure learning in Bayesian networks using regular vines
- Vines -- a new graphical model for dependent random variables.
- Probability density decomposition for conditionally dependent random variables modeled by vines
- Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50
- Pair-copula constructions for non-Gaussian DAG models
- Uncertainty Analysis with High Dimensional Dependence Modelling
- Identifiability of Gaussian structural equation models with equal error variances
- Tuning Parameter Selection in High Dimensional Penalized Likelihood
- On Information and Sufficiency