We derive distributional limits for empirical transport distances between probability measures supported on countable sets. Our approach is based on sensitivity analysis of optimal values of infinite dimensional mathematical programs and a delta method for nonlinear derivatives. A careful calibration of the norm on the space of probability measures is needed in order to combine differentiability and weak convergence of the underlying empirical process. Based on this, we provide a sufficient and necessary condition for the underlying distribution on the countable metric space for such a distributional limit to hold. We give an explicit form of the limiting distribution for tree spaces.
Finally, we apply our findings to optimal transport based inference in large scale problems. An application to nanoscale microscopy is given.
"Empirical optimal transport on countable metric spaces: Distributional limits and statistical applications." Ann. Appl. Probab. 29 (5) 2744 - 2781, October 2019. https://doi.org/10.1214/19-AAP1463