A multivariate spatial regression model using signatures
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913941472411648 |
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| author | Frévent, Camille Dabo, Issa-Mbenard |
| author_facet | Frévent, Camille Dabo, Issa-Mbenard |
| contents | We propose a spatial autoregressive model for a multivariate response variable and functional covariates. The approach is based on the notion of signature, which represents a function as an infinite series of its iterated integrals and presents the advantage of being applicable to a wide range of processes. We have provided theoretical guarantees for the choice of the signature truncation order, and we have shown in a simulation study and an application to pollution data that this approach outperforms existing approaches in the literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A multivariate spatial regression model using signatures Frévent, Camille Dabo, Issa-Mbenard Statistics Theory Methodology We propose a spatial autoregressive model for a multivariate response variable and functional covariates. The approach is based on the notion of signature, which represents a function as an infinite series of its iterated integrals and presents the advantage of being applicable to a wide range of processes. We have provided theoretical guarantees for the choice of the signature truncation order, and we have shown in a simulation study and an application to pollution data that this approach outperforms existing approaches in the literature. |
| title | A multivariate spatial regression model using signatures |
| topic | Statistics Theory Methodology |
| url | https://arxiv.org/abs/2410.07899 |