Conformal prediction for functional Ordinary kriging
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912051523223552 |
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| author | De Magistris, Anna Diana, Andrea Romano, Elvira |
| author_facet | De Magistris, Anna Diana, Andrea Romano, Elvira |
| contents | Functional Ordinary Kriging is the most widely used method to predict a curve at a given spatial point. However, uncertainty remains an open issue. In this article a distribution-free prediction method based on two different modulation functions and two conformity scores is proposed. Through simulations and benchmark data analyses, we demonstrate the advantages of our approach when compared to standard methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20084 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conformal prediction for functional Ordinary kriging De Magistris, Anna Diana, Andrea Romano, Elvira Methodology Functional Ordinary Kriging is the most widely used method to predict a curve at a given spatial point. However, uncertainty remains an open issue. In this article a distribution-free prediction method based on two different modulation functions and two conformity scores is proposed. Through simulations and benchmark data analyses, we demonstrate the advantages of our approach when compared to standard methods. |
| title | Conformal prediction for functional Ordinary kriging |
| topic | Methodology |
| url | https://arxiv.org/abs/2409.20084 |