Local Fréchet regression with toroidal predictors

Fuente: arXiv
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Main Authors: Im, Chang Jun, Jeon, Jeong Min
Format: Preprint
Published: 2026
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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