Large deviations for symmetrised empirical measures

TL;DR

This paper establishes a Large Deviation Principle for symmetrised empirical measures involving random permutations, enhancing understanding of their probabilistic behavior and improving existing principles for related bridge processes.

Contribution

It introduces a Large Deviation Principle for symmetrised empirical measures with random permutations, extending theoretical understanding in this area.

Findings

Proves a Large Deviation Principle for symmetrised empirical measures.

Improves existing Large Deviation Principles for initial-terminal bridge processes.

Abstract

In this paper we prove a Large Deviation Principle for the sequence of symmetrised empirical measures $\frac{1}{n} \sum_{i=1}^{n} \delta_{(X^n_i,X^n_{\sigma_n(i)})}$ where $\sigma_n$ is a random permutation and $((X_i^n)_{1 \leq i \leq n})_{n \geq 1}$ is a triangular array of random variables with suitable properties. As an application we show how this result allows to improve the Large Deviation Principles for symmetrised initial-terminal conditions bridge processes recently established by Adams, Dorlas and K\"{o}nig.