Weighted covariance matrix estimation
The paper proposes a cross-validated linear shrinkage estimation for population covariance matrices. Moreover we also propose a novel weighted estimator based on the thresholding and shrinkage methods for high dimensional datasets. It is applicable to a wider scope of different structures of covaria...
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sg-ntu-dr.10356-1444592023-02-28T19:22:04Z Weighted covariance matrix estimation Yang, Guangren Liu, Yiming Pan, Guangming School of Physical and Mathematical Sciences Science::Mathematics Thresholding Shrinkage The paper proposes a cross-validated linear shrinkage estimation for population covariance matrices. Moreover we also propose a novel weighted estimator based on the thresholding and shrinkage methods for high dimensional datasets. It is applicable to a wider scope of different structures of covariance matrices. Some theoretical results about the cross-validated shrinkage method and weighted covariance estimation methods are also developed. The finite-sample performance of the proposed methods is illustrated through extensive simulations and real data analysis. Accepted version 2020-11-06T03:03:27Z 2020-11-06T03:03:27Z 2019 Journal Article Yang, G., Liu, Y., & Pan, G. (2019). Weighted covariance matrix estimation. Computational Statistics & Data Analysis, 139, 82–98. doi:10.1016/j.csda.2019.04.017 0167-9473 https://hdl.handle.net/10356/144459 10.1016/j.csda.2019.04.017 139 82 98 en Computational Statistics & Data Analysis © 2019 Elsevier B.V. All rights reserved. This paper was published in Computational Statistics & Data Analysis and is made available with permission of Elsevier B.V. application/pdf |
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Science::Mathematics Thresholding Shrinkage |
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Science::Mathematics Thresholding Shrinkage Yang, Guangren Liu, Yiming Pan, Guangming Weighted covariance matrix estimation |
| description |
The paper proposes a cross-validated linear shrinkage estimation for population covariance matrices. Moreover we also propose a novel weighted estimator based on the thresholding and shrinkage methods for high dimensional datasets. It is applicable to a wider scope of different structures of covariance matrices. Some theoretical results about the cross-validated shrinkage method and weighted covariance estimation methods are also developed. The finite-sample performance of the proposed methods is illustrated through extensive simulations and real data analysis. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Yang, Guangren Liu, Yiming Pan, Guangming |
| format |
Article |
| author |
Yang, Guangren Liu, Yiming Pan, Guangming |
| author_sort |
Yang, Guangren |
| title |
Weighted covariance matrix estimation |
| title_short |
Weighted covariance matrix estimation |
| title_full |
Weighted covariance matrix estimation |
| title_fullStr |
Weighted covariance matrix estimation |
| title_full_unstemmed |
Weighted covariance matrix estimation |
| title_sort |
weighted covariance matrix estimation |
| publishDate |
2020 |
| url |
https://hdl.handle.net/10356/144459 |
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1759858224291381248 |