An improved banded estimation for large covariance matrix
Wenyu Yang and
Xiaoning Kang
Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 1, 141-155
Abstract:
The modified Cholesky decomposition (MCD) is a powerful and efficient tool for the large covariance matrix estimation, which guarantees the positive definite property of the estimated matrix. However, when implementing the MCD, it requires a pre-knowledge of the variable ordering, which is often unknown before analysis or does not exist for some real data. In this work, we propose a positive definite Cholesky-based estimate for the large banded covariance matrix by recovering the variable ordering before applying the MCD technique. The asymptotically theoretical convergence rate is established under some regularity conditions. The merits of the proposed model is illustrated by simulation study and applications to two gene expression data sets.
Date: 2023
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.1910839 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:52:y:2023:i:1:p:141-155
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2021.1910839
Access Statistics for this article
Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe
More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().