A Cholesky-based sparse covariance estimation with an application to genes data. Issue 5 (3rd September 2021)

• Record Type: Journal Article
• Authors: Li, Chunshi; Yang, Mo; Wang, Mingqiu; Kang, Hong; Kang, Xiaoning
• Is Part Of: Journal of biopharmaceutical statistics. Volume 31:Issue 5(2021)
• Page Start: 603
• Page End: 616
• Publication Date: 2021-09-03
• Subjects: Covariance matrix -- gene expressions -- model averaging -- modified Cholesky decomposition -- sparsity; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2021.1931270
• Languages: English
• ISSNs: 1054-3406

Abstract

ABSTRACT: The modified Cholesky decomposition (MCD) is a powerful tool for estimating a covariance matrix. The regularization can be conveniently imposed on the linear regressions to encourage the sparsity in the estimated covariance matrix to accommodate the high-dimensional data. In this paper, we propose a Cholesky-based sparse ensemble estimate for covariance matrix by averaging a set of Cholesky factor estimates obtained from multiple variable orderings used in the MCD. The sparse estimation is enabled by encouraging the sparsity in the Cholesky factor. The theoretical consistent property is established under some regular conditions. The merits of the proposed method are illustrated through simulation and a maize genes data set.