Graphical lasso for extremes
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917404597026816 |
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| author | Wan, Phyllis Zhou, Chen |
| author_facet | Wan, Phyllis Zhou, Chen |
| contents | In this paper, we estimate the sparse dependence structure in the tail region of a multivariate random vector, potentially of high dimension. The tail dependence is modeled via a graphical model for extremes embedded in the Hüsler-Reiss distribution. We propose the extreme graphical lasso procedure to estimate the sparsity in the tail dependence, similar to the Gaussian graphical lasso in high dimensional statistics. We prove its consistency in identifying the graph structure and estimating model parameters. The efficiency and accuracy of the proposed method are illustrated by simulations and real data examples. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2307_15004 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Graphical lasso for extremes Wan, Phyllis Zhou, Chen Methodology Statistics Theory 62G32, 62H12, 62F12 In this paper, we estimate the sparse dependence structure in the tail region of a multivariate random vector, potentially of high dimension. The tail dependence is modeled via a graphical model for extremes embedded in the Hüsler-Reiss distribution. We propose the extreme graphical lasso procedure to estimate the sparsity in the tail dependence, similar to the Gaussian graphical lasso in high dimensional statistics. We prove its consistency in identifying the graph structure and estimating model parameters. The efficiency and accuracy of the proposed method are illustrated by simulations and real data examples. |
| title | Graphical lasso for extremes |
| topic | Methodology Statistics Theory 62G32, 62H12, 62F12 |
| url | https://arxiv.org/abs/2307.15004 |