Local Fr\'echet regression with circular predictors
Abstract
Fr\'echet regression extends the principles of linear regression to accommodate responses valued in generic metric spaces. While this approach has primarily focused on exploring relationships between Euclidean predictors and non-Euclidean responses, our work introduces a novel statistical method for handling random objects with circular predictors. We concentrate on local constant and local linear Fr\'echet regression, providing rigorous proofs for the upper bounds of both bias and stochastic deviation of the estimators under mild conditions. This research lays the groundwork for broadening the application of Fr\'echet regression to scenarios involving non-Euclidean covariates, thereby expanding its utility in complex data analysis.
Cite
@article{arxiv.2408.10118,
title = {Local Fr\'echet regression with circular predictors},
author = {Chang Jun Im and Jeong Min Jeon},
journal= {arXiv preprint arXiv:2408.10118},
year = {2026}
}
Comments
The case for circular predictors is containted in the case for spherical preditors