Integrating additional knowledge into the estimation of graphical models
From MaRDI portal
Publication:6637073
DOI10.1515/ijb-2020-0133MaRDI QIDQ6637073
Yunqi Bu, Johannes Christof Lederer
Publication date: 13 November 2024
Published in: The International Journal of Biostatistics (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Confidence intervals for high-dimensional inverse covariance estimation
- Sparse inverse covariance estimation with the graphical lasso
- The Adaptive Lasso and Its Oracle Properties
- Stability
- On the prediction performance of the Lasso
- Statistics for high-dimensional data. Methods, theory and applications.
- A survey of cross-validation procedures for model selection
- Oracle inequalities for high-dimensional prediction
- On the conditions used to prove oracle results for the Lasso
- Prediction error bounds for linear regression with the TREX
- High-dimensional graphs and variable selection with the Lasso
- A permutation approach for selecting the penalty parameter in penalized model selection
- A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees
- Emergence of Scaling in Random Networks
- Model selection and estimation in the Gaussian graphical model
- The Bayesian Lasso
- Checking the Independence of Two Covariance-Stationary Time Series: A Univariate Residual Cross-Correlation Approach
- Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
- A Theorem about Random Fields
This page was built for publication: Integrating additional knowledge into the estimation of graphical models