Wasserstein convergence rates for empirical measures of point processes

TL;DR

This paper derives sharp bounds on how quickly empirical measures of point processes converge under Wasserstein distance, introducing a new metric and providing tools for statistical inference.

Contribution

It introduces a new metric on counting measures, defines a Wasserstein distance for point processes, and establishes convergence rates with concentration results.

Findings

Established sharp upper and lower bounds on convergence rates.

Introduced a new metric on the space of counting measures.

Provided concentration results for empirical measures.

Abstract

In this paper, we establish sharp upper and lower bounds on the convergence rate of the empirical measures of point processes under the Wasserstein distance. To this end, we first introduce a new metric on the space of counting measures and, based on this metric, define a Wasserstein distance between point processes. We then employ it to study the convergence rate of the empirical measures of point processes, which serves as a natural tool for identifying the distribution of the underlying point process. Furthermore, we derive concentration results. These theoretical results provide constructive tools for hypothesis testing and statistical inference for point processes. The applicability of our results is demonstrated through several practical examples.