| Autor: |
Konishi, Keisuke, Yata, Kazuyoshi, Aoshima, Makoto |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
数理解析研究所講究録. 2157:11-20 |
| ISSN: |
1880-2818 |
| Popis: |
In this paper, we consider the estimation for the inverse matrix of a high-dimensional covariance matrix under the strongly spiked eigenvalue model. One of the well-known estimation methods is the principal orthogonal complement thresholding (POET) given by Fan et al. [5]. We show that the POET has consistency properties only under several severe conditions in high-dimensional settings. In order to overcome the difficulty, we consider applying the noise-reduction (NR) method given by Yata and Aoshima [8, 9] to the POET. We propose a new estimation of the inverse covariance matrix called the NR-POET. We compare the performance of the NR-POET with the POET by several simulations. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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