Random-Effects Algorithm for Random Objects in Metric Spaces
Across many scientific disciplines, multiple observations are collected from the same experimental units, and in modern datasets these observations often arise as non-Euclidean random objects. In such settings, the incorporation of random effects is a critical modeling step for efficient estimation and personalized prediction. Although mixed-effects models are well established for scalar outcomes and, more recently, for functional data in Hilbert spaces, general random-effects frameworks for objects in metric spaces remain underdeveloped. In this paper, we propose a nonlinear Fréchet-based algorithm for random-effects modeling of arbitrary random objects defined on a metric space. Using M...
| Authors | Marcos Matabuena, Mateo Cámara |
|---|---|
| Primary category | stat.ML |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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