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Regularized estimation of Kronecker structured covariance matrix using modified Cholesky decomposition

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Názov: Regularized estimation of Kronecker structured covariance matrix using modified Cholesky decomposition
Autori: Dai, Deliang, Hao, Chengcheng, Jin, Shaobo, 1987, Liang, Yuli
Zdroj: Journal of Statistical Computation and Simulation. 95(5):905-930
Predmety: Covariance matrix estimation, Kronecker structure, multivariate longitudinal data, modified Cholesky decomposition, regularization
Popis: In this paper, we study a Kronecker structured model for covariance matrices when data are matrix-valued. Using the modified Cholesky decomposition for Kronecker structured covariance matrix, we propose a regularized covariance estimator by imposing shrinkage and smoothing penalties on the Cholesky factors. A regularized flip-flop (RFF) algorithm is developed to produce a statistically efficient estimator for a large covariance matrix of matrix-valued data. Asymptotic properties are investigated and the performance of the estimator is evaluated by simulations. The results presented are applied to real data example.
Popis súboru: electronic
Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-556953
https://doi.org/10.1080/00949655.2023.2291536
Databáza: SwePub
Popis
Abstrakt:In this paper, we study a Kronecker structured model for covariance matrices when data are matrix-valued. Using the modified Cholesky decomposition for Kronecker structured covariance matrix, we propose a regularized covariance estimator by imposing shrinkage and smoothing penalties on the Cholesky factors. A regularized flip-flop (RFF) algorithm is developed to produce a statistically efficient estimator for a large covariance matrix of matrix-valued data. Asymptotic properties are investigated and the performance of the estimator is evaluated by simulations. The results presented are applied to real data example.
ISSN:00949655
15635163
DOI:10.1080/00949655.2023.2291536