Central limit theorems for $U$-statistics of Poisson point processes

Abstract

A $U$-statistic of a Poisson point process is defined as the sum $\sum f(x_{1},\ldots,x_{k})$ over all (possibly infinitely many) $k$-tuples of distinct points of the point process. Using the Malliavin calculus, the Wiener–Itô chaos expansion of such a functional is computed and used to derive a formula for the variance. Central limit theorems for $U$-statistics of Poisson point processes are shown, with explicit bounds for the Wasserstein distance to a Gaussian random variable. As applications, the intersection process of Poisson hyperplanes and the length of a random geometric graph are investigated.