Global Fr\'echet regression from time correlated bivariate curve data in manifolds
A. Torres-Signes, M. P. Fr\'ias, M. D. Ruiz-Medina

TL;DR
This paper develops a method for global Fréchet regression on manifold-valued bivariate curve data, providing consistent predictions in a time-correlated setting with applications to satellite magnetic field data.
Contribution
It introduces a novel Fréchet regression framework for time-correlated manifold-valued curves, ensuring consistency and applicability to real-world geospatial data.
Findings
The proposed predictor is uniformly weakly consistent under certain regularity conditions.
Simulation on the sphere demonstrates good finite sample performance.
Application to satellite data predicts magnetic field coordinates effectively.
Abstract
Global Fr\'echet regression is addressed from the observation of a strictly stationary bivariate curve process, evaluated in a finite--dimensional compact differentiable Riemannian manifold, with bounded positive smooth sectional curvature. The involved univariate curve processes respectively define the functional response and regressor, having the same Fr\'echet functional mean. The supports of the marginal probability measures of the regressor and response processes are assumed to be contained in a ball, whose radius ensures the injectivity of the exponential map. This map has time--varying origin at the common marginal Fr\'echet functional mean. A weighted Fr\'echet mean approach is adopted in the definition of the theoretical loss function. The regularized Fr\'echet weights are computed, in the time--varying tangent space from the log--mapped regressors. Under these assumptions, and…
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Taxonomy
TopicsStatistical Mechanics and Entropy · Statistical Methods and Inference · Gaussian Processes and Bayesian Inference
