Local Fréchet regression with toroidal predictors
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912923006271488 |
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| author | Im, Chang Jun Jeon, Jeong Min |
| author_facet | Im, Chang Jun Jeon, Jeong Min |
| contents | We provide the first regression framework that simultaneously accommodates responses taking values in a general metric space and predictors lying on a general torus. We propose intrinsic local constant and local linear estimators that respect the underlying geometries of both the response and predictor spaces. Our local linear estimator is novel even in the case of scalar responses. We further establish their asymptotic properties, including consistency and convergence rates. Simulation studies, together with an application to real data, illustrate the superior performance of the proposed methodology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20572 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Local Fréchet regression with toroidal predictors Im, Chang Jun Jeon, Jeong Min Methodology Statistics Theory 62G08, 62R02, 62H11, 62G20, 62R01 We provide the first regression framework that simultaneously accommodates responses taking values in a general metric space and predictors lying on a general torus. We propose intrinsic local constant and local linear estimators that respect the underlying geometries of both the response and predictor spaces. Our local linear estimator is novel even in the case of scalar responses. We further establish their asymptotic properties, including consistency and convergence rates. Simulation studies, together with an application to real data, illustrate the superior performance of the proposed methodology. |
| title | Local Fréchet regression with toroidal predictors |
| topic | Methodology Statistics Theory 62G08, 62R02, 62H11, 62G20, 62R01 |
| url | https://arxiv.org/abs/2602.20572 |