Normal approximation for stabilizing functionals

Citation
Lachièze-rey Raphaël et al., Normal approximation for stabilizing functionals, Annals of applied probability , 29(2), 2019, pp. 931-993
ISSN journal
10505164
Volume
29
Issue
2
Year of publication
2019
Pages
931 - 993
Database
ACNP
SICI code
Abstract
We establish presumably optimal rates of normal convergence with respect to the Kolmogorov distance for a large class of geometric functionals of marked Poisson and binomial point processes on general metric spaces.The rates are valid whenever the geometric functional is expressible as a sum of exponentially stabilizing score functions satisfying a moment condition.By incorporating stabilization methods into the Malliavin.Stein theory, we obtain rates of normal approximation for sums of stabilizing score functions which either improve upon existing rates or are the first of their kind.Our general rates hold for functionals of marked input on spaces more general than full-dimensional subsets of .d, including m-dimensional Riemannian manifolds, m.d.We use the general results to deduce improved and new rates of normal convergence for several functionals in stochastic geometry, including those whose variances re-scale as the volume or the surface area of an underlying set.In particular, we improve upon rates of normal convergence for the k-face and i th intrinsic volume functionals of the convex hull of Poisson and binomial random samples in a smooth convex body in dimension d.2.We also provide improved rates of normal convergence for statistics of nearest neighbors graphs and high-dimensional data sets, the number of maximal points in a random sample, estimators of surface area and volume arising in set approximation via Voronoi tessellations, and clique counts in generalized random geometric graphs.