Change Point Detection for Functional Autoregressive Processes on the Sphere
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915650876735488 |
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| author | Spoto, Federica Caponera, Alessia Brutti, Pierpaolo |
| author_facet | Spoto, Federica Caponera, Alessia Brutti, Pierpaolo |
| contents | We introduce a novel framework for change point detection in spherical functional autoregressive (SPHAR) processes, enabling the identification of structural breaks in spatio-temporal random fields on the sphere. Our LASSO-regularized estimator, based on penalized dynamic programming in the harmonic domain, operates without knowledge of the number or locations of change points and offers non-asymptotic theoretical guarantees. This approach provides a new tool for analyzing nonstationary phenomena on the sphere, relevant to climate science, cosmology, and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03255 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Change Point Detection for Functional Autoregressive Processes on the Sphere Spoto, Federica Caponera, Alessia Brutti, Pierpaolo Methodology We introduce a novel framework for change point detection in spherical functional autoregressive (SPHAR) processes, enabling the identification of structural breaks in spatio-temporal random fields on the sphere. Our LASSO-regularized estimator, based on penalized dynamic programming in the harmonic domain, operates without knowledge of the number or locations of change points and offers non-asymptotic theoretical guarantees. This approach provides a new tool for analyzing nonstationary phenomena on the sphere, relevant to climate science, cosmology, and beyond. |
| title | Change Point Detection for Functional Autoregressive Processes on the Sphere |
| topic | Methodology |
| url | https://arxiv.org/abs/2512.03255 |