Uniform Consistency of Generalized Fréchet Means
arXiv:2408.07534v2 Announce Type: replace-cross Abstract: Loss-based notions of centre on nonlinear spaces range from the Fréchet mean and power means to the geometric median and, in a limiting sense, the Chebyshev centre. To use such summaries statistically, one first needs a law of large numbers that remains valid beyond smooth manifolds and beyond a fixed choice of loss. We study generalized Fréchet means on metric spaces with the Heine–Borel property, obtained by replacing squared distance with a convex loss under a mild exponential-growth condition. We prove existence and compactness of the population mean set, establish a sharp diameter bound, obtain almost-sure consistency of empirical $\phi$-means, and derive a uniform strong law over compact classes of losses. The analysis is driven by a deterministic argmin principle together with a Glivenko–Cantelli theorem for monotone classes. For isotropic densities on Riemannian symmetric spaces, we identify the population $\phi$-mean for every strictly increasing loss for which the objective is finite, including bounded robust losses. We also illustrate the framework on spheres and on the polyhedral space of ultrametric phylogenetic trees.