Empirical optimal transport on countable metric spaces: Distributional limits and statistical applications
2019 | journal article; research paper. A publication with affiliation to the University of Göttingen.
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- Authors
- Tameling, Carla; Sommerfeld, Max; Munk, Axel
- Abstract
- 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 non-linear 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 ultra-metric spaces. Finally, we apply our findings to optimal transport based inference in large scale problems. An application to nanoscale microscopy is given.
- Issue Date
- 2019
- Journal
- The Annals of Applied Probability
- Project
- RTG 2088: Research Training Group 2088 Discovering structure in complex data: Statistics meets Optimization and Inverse Problems
EXC 2067: Multiscale Bioimaging - Working Group
- RG Munk
- Language
- English