Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | R-NL: covariance matrix estimation for elliptical distributions based on nonlinear shrinkage |
| Organization Unit | |
| Authors |
|
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
|
| Journal Title | IEEE Transactions on Signal Processing |
| Publisher | Institute of Electrical and Electronics Engineers |
| Geographical Reach | international |
| ISSN | 1053-587X |
| Volume | 71 |
| Page Range | 1657 - 1668 |
| Date | 2023 |
| Abstract Text | We combine Tyler's robust estimator of the dispersion matrix with nonlinear shrinkage. This approach delivers a simple and fast estimator of the dispersion matrix in elliptical models that is robust against both heavy tails and high dimensions. We prove convergence of the iterative part of our algorithm and demonstrate the favorable performance of the estimator in a wide range of simulation scenarios. Finally, an empirical application demonstrates its state-of-the-art performance on real data. |
| Related URLs | |
| Digital Object Identifier | 10.1109/tsp.2023.3270742 |
| Other Identification Number | merlin-id:24299 |
| PDF File |
Download from ZORA
|
| Export |
BibTeX
EP3 XML (ZORA)
|
| Keywords | Electrical and electronic engineering, signal processing, heavy tails, nonlinear shrinkage, portfolio optimization |
| Additional Information | Auch publiziert bei ArXiv.org (10.48550/arXiv.2210.14854). |