Model selection for extremal dependence structures using deep learning: Application to environmental data
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916402653298688 |
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| author | Ahmed, Manaf Maume-Deschamps, Véronique Ribereau, Pierre |
| author_facet | Ahmed, Manaf Maume-Deschamps, Véronique Ribereau, Pierre |
| contents | This paper introduces a new methodology for extreme spatial dependence structure selection. It is based on deep learning techniques, specifically Convolutional Neural Networks -CNNs. Two schemes are considered: in the first scheme, the matching probability is evaluated through a single CNN while in the second scheme, a hierarchical procedure is proposed: a first CNN is used to select a max-stable model, then another network allows to select the most adapted covariance function, according to the selected max-stable model. This model selection approach demonstrates performs very well on simulations. In contrast, the Composite Likelihood Information Criterion CLIC faces issues in selecting the correct model. Both schemes are applied to a dataset of 2m air temperature over Iraq land, CNNs are trained on dependence structures summarized by the Concurrence probability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13276 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model selection for extremal dependence structures using deep learning: Application to environmental data Ahmed, Manaf Maume-Deschamps, Véronique Ribereau, Pierre Data Analysis, Statistics and Probability This paper introduces a new methodology for extreme spatial dependence structure selection. It is based on deep learning techniques, specifically Convolutional Neural Networks -CNNs. Two schemes are considered: in the first scheme, the matching probability is evaluated through a single CNN while in the second scheme, a hierarchical procedure is proposed: a first CNN is used to select a max-stable model, then another network allows to select the most adapted covariance function, according to the selected max-stable model. This model selection approach demonstrates performs very well on simulations. In contrast, the Composite Likelihood Information Criterion CLIC faces issues in selecting the correct model. Both schemes are applied to a dataset of 2m air temperature over Iraq land, CNNs are trained on dependence structures summarized by the Concurrence probability. |
| title | Model selection for extremal dependence structures using deep learning: Application to environmental data |
| topic | Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2409.13276 |