Core shrinkage covariance estimation for matrix-variate data
نویسندگان
چکیده
Abstract A separable covariance model can describe the among-row and among-column correlations of a random matrix permits likelihood-based inference with very small sample size. However, if assumption separability is not met, data analysis may misrepresent important dependence patterns in data. As compromise between unstructured estimation, we decompose into component complementary ‘core’ matrix. This decomposition defines new that makes use parsimony interpretability model, yet fully describes matrices are non-separable. motivates type shrinkage estimator, obtained by appropriately shrinking core matrix, adapts to degree population
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ژورنال
عنوان ژورنال: Journal of The Royal Statistical Society Series B-statistical Methodology
سال: 2023
ISSN: ['1467-9868', '1369-7412']
DOI: https://doi.org/10.1093/jrsssb/qkad070