Network Weighted Functional Regression: a method for modeling dependencies between functional data in a network
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915312860921856 |
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| author | Romano, Elvira Irpino, Antonio Miller, Claire |
| author_facet | Romano, Elvira Irpino, Antonio Miller, Claire |
| contents | In this paper, we propose a Network-Weighted Functional Regression (NWFR) model, an extension of Spatially Weighted Functional Regression (SWFR) to functional data defined on network-structured settings. To asses predictive uncertainity, we develop a functional conformal prediction procedure that yields a distribution free prediction intervals with guaranteed coverage. Through extensive evaluation on both simulated and real-world datasets, we demonstrate that the explicit modeling of network structure yields substantive improvements in point-prediction accuracy and markedly enhances the validity and precision of the resulting prediction intervals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18221 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Network Weighted Functional Regression: a method for modeling dependencies between functional data in a network Romano, Elvira Irpino, Antonio Miller, Claire Methodology In this paper, we propose a Network-Weighted Functional Regression (NWFR) model, an extension of Spatially Weighted Functional Regression (SWFR) to functional data defined on network-structured settings. To asses predictive uncertainity, we develop a functional conformal prediction procedure that yields a distribution free prediction intervals with guaranteed coverage. Through extensive evaluation on both simulated and real-world datasets, we demonstrate that the explicit modeling of network structure yields substantive improvements in point-prediction accuracy and markedly enhances the validity and precision of the resulting prediction intervals. |
| title | Network Weighted Functional Regression: a method for modeling dependencies between functional data in a network |
| topic | Methodology |
| url | https://arxiv.org/abs/2501.18221 |