High-dimensional covariance estimation for Gaussian directed acyclic graph models with given order
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Publication:6601077
DOI10.1002/wics.1468zbMATH Open1544.62145MaRDI QIDQ6601077
Publication date: 10 September 2024
Published in: Wiley Interdisciplinary Reviews. WIREs Computational Statistics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
- Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs
- \(\ell_{0}\)-penalized maximum likelihood for sparse directed acyclic graphs
- Covariance estimation: the GLM and regularization perspectives
- A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data
- Regularized estimation of large covariance matrices
- Sparse estimation of large covariance matrices via a nested Lasso penalty
- Wishart distributions for decomposable graphs
- Nonparametric estimation of large covariance matrices of longitudinal data
- Objective Bayes Covariate‐Adjusted Sparse Graphical Model Selection
- Learning Local Dependence In Ordered Data
- Covariance matrix selection and estimation via penalised normal likelihood
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