Estimation of high-dimensional seemingly unrelated regression models
DOI10.1080/07474938.2021.1889195zbMath1491.62041arXiv1811.05567OpenAlexW3193134839MaRDI QIDQ5861052
Hyungsik Roger Moon, Lidan Tan, Khai Xiang Chiong
Publication date: 4 March 2022
Published in: Econometric Reviews (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1811.05567
precision matrixgraphical Lassoseemingly unrelated regressionfeasible graphical Lasso estimatorhigh-dimensional matrix estimation
Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic distribution theory in statistics (62E20) Applications of statistics to biology and medical sciences; meta analysis (62P10)
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- Sparse estimation of a covariance matrix
- Sparse inverse covariance estimation with the graphical lasso
- Sparsistency and rates of convergence in large covariance matrix estimation
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Structure estimation for discrete graphical models: generalized covariance matrices and their inverses
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- High-Dimensional Probability
- Estimation of graphical models using the L1,2 norm
- On Consistency and Sparsity for Principal Components Analysis in High Dimensions
- An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
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