The fourth moment theorem on the Poisson space

TL;DR

This paper establishes an exact fourth moment bound for normal approximation on the Poisson space, extending the 'fourth moment phenomenon' from Gaussian to Poisson frameworks using advanced probabilistic tools.

Contribution

It introduces a novel approach employing carré-du-champ operators and Markov generator techniques to analyze the fourth moment phenomenon in Poisson spaces, a significant extension of prior Gaussian-focused results.

Findings

Proves an exact fourth moment bound for Poisson chaos

Demonstrates the emergence of the fourth moment phenomenon in Poisson settings

Provides new bounds for Gamma approximation of Poisson functionals

Abstract

We prove an exact fourth moment bound for the normal approximation of random variables belonging to the Wiener chaos of a general Poisson random measure. Such a result -- that has been elusive for several years -- shows that the so-called `fourth moment phenomenon', first discovered by Nualart and Peccati (2005) in the context of Gaussian fields, also systematically emerges in a Poisson framework. Our main findings are based on Stein's method, Malliavin calculus and Mecke-type formulae, as well as on a methodological breakthrough, consisting in the use of carr\'e-du-champ operators on the Poisson space for controlling residual terms associated with add-one cost operators. Our approach can be regarded as a successful application of Markov generator techniques to probabilistic approximations in a non-diffusive framework: as such, it represents a significant extension of the seminal contributions by Ledoux (2012) and Azmoodeh, Campese and Poly (2014). To demonstrate the flexibility of our results, we also provide some novel bounds for the Gamma approximation of non-linear functionals of a Poisson measure.