A shrinkage approach to joint estimation of multiple covariance matrices
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Publication:2036300
DOI10.1007/s00184-020-00781-3zbMath1467.62087OpenAlexW3036335948MaRDI QIDQ2036300
Kai Dong, Zongliang Hu, Yuedong Wang, Zhishui Hu, Tie Jun Tong
Publication date: 28 June 2021
Published in: Metrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00184-020-00781-3
covariance matricesshrinkage parameterquadratic loss functionStein loss functionjoint estimationoptimal estimator
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Cites Work
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- A well-conditioned estimator for large-dimensional covariance matrices
- Nonlinear shrinkage estimation of large-dimensional covariance matrices
- James-Stein type estimators of variances
- Simultaneous modelling of the Cholesky decomposition of several covariance matrices
- Large random matrices: Lectures on macroscopic asymptotics. École d'Été des Probabilités de Saint-Flour XXXVI -- 2006
- Empirical Bayes estimation of the multivariate normal covariance matrix
- The variational form of certain Bayes estimators
- Estimation of a covariance matrix using the reference prior
- Bayesian modeling of several covariance matrices and some results on propriety of the posterior for linear regression with correlated and/or heterogeneous errors
- Joint estimation of multiple high-dimensional precision matrices
- Principal component models for correlation matrices
- Joint estimation of multiple graphical models
- The comparison of sample covariance matrices using likelihood ratio tests
- Spectral models for covariance matrices
- A Hierarchical Eigenmodel for Pooled Covariance Estimation
- All Invariant Moments of the Wishart Distribution
- The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
- Optimal Shrinkage Estimation of Variances With Applications to Microarray Data Analysis
