Golyandina, Nina and Zhigljavsky, Anatoly  ORCID: https://orcid.org/0000-0003-0630-8279
2020.
Blind deconvolution of covariance matrix inverses for autoregressive processes.
Linear Algebra and its Applications
593
, pp. 188-211.
10.1016/j.laa.2020.02.005
|
Preview |
PDF
- Accepted Post-Print Version
Download (347kB) | Preview |
Abstract
Matrix C can be blindly deconvoluted if there exist matrices A and B such that C = A * B, where * denotes the operation of matrix convolution. We study the prob- lem of matrix deconvolution in the case where matrix C is proportional to the inverse of the autocovariance matrix of an autoregressive process. We show that the deconvolution of such matrices is important in problems of Hankel structured low-rank approximation (HSLRA). In the cases of autoregressive models of orders one and two, we fully charac- terize the range of parameters where such deconvolution can be performed and provide construction schemes for performing deconvolutions. We also consider general autoregres- sive models of order p, where we prove that the deconvolution C = A * B does not exist if the matrix B is diagonal and its size is larger than p.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Mathematics |
| Publisher: | Elsevier |
| ISSN: | 0024-3795 |
| Funders: | no |
| Date of First Compliant Deposit: | 26 February 2020 |
| Date of Acceptance: | 5 February 2020 |
| Last Modified: | 23 Nov 2024 10:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/129987 |
Citation Data
Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
 |
Edit Item |
Altmetric
Altmetric