U-statistics in stochastic geometry

A U-statistic of order k with kernel f :X →Rd over a Poisson process is defined in [25] as ∑ x1,...,xk∈η 6= f (x1, . . . ,xk) under appropriate integrability assumptions on f . U-statistics play an important role in stochastic geometry since many interesting functionals can be written as Ustatistics, like intrinsic volumes of intersection processes, characteristics of random geometric graphs, volumes of random simplices, and many others, see for instance [15, 18, 25]. It turns out that the Wiener-Ito chaos expansion of a U-statistic is finite and thus Malliavin calculus is a particularly suitable method. Variance estimates, the approximation of the covariance structure and limit theorems which have been out of reach for many years can be derived. In this chapter we state the fundamental properties of U-statistics and investigate moment formulae. The main object of the chapter is to introduce the available limit theorems. 1 U-statistics and decompositions 1.1 Definition Let X be a Polish space, k ≥ 1, and f : X → R be a measurable function. The U-statistic of order k with kernel f over a configuration η ∈ Ns(X) is 0 if η has strictly less than k points and the formal sum Name of First Author Name, Address of Institute, e-mail: [email protected] Name of Second Author Name, Address of Institute e-mail: [email protected]