Random-Effects Algorithm for Random Objects in Metric Spaces

ArXi:2605.02693v1 Announce Type: cross 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, recently, for functional data in Hilbert spaces, general random-effects frameworks for objects in metric spaces remain underdeveloped.