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- W4384834013 abstract "Abstract A separable covariance model can describe the among-row and among-column correlations of a random matrix and permits likelihood-based inference with a very small sample size. However, if the assumption of separability is not met, data analysis with a separable model may misrepresent important dependence patterns in the data. As a compromise between separable and unstructured covariance estimation, we decompose a covariance matrix into a separable component and a complementary ‘core’ covariance matrix. This decomposition defines a new covariance matrix decomposition that makes use of the parsimony and interpretability of a separable covariance model, yet fully describes covariance matrices that are non-separable. This decomposition motivates a new type of shrinkage estimator, obtained by appropriately shrinking the core of the sample covariance matrix, that adapts to the degree of separability of the population covariance matrix." @default.
- W4384834013 created "2023-07-21" @default.
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- W4384834013 date "2023-07-20" @default.
- W4384834013 modified "2023-10-17" @default.
- W4384834013 title "Core shrinkage covariance estimation for matrix-variate data" @default.
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- W4384834013 doi "https://doi.org/10.1093/jrsssb/qkad070" @default.
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