Surface order scaling in stochastic geometry
arXiv:1312.6595 · doi:10.1214/13-AAP992
Abstract
Let denote a Poisson point process of intensity on , with a bounded density on and . Given a closed subset of Hausdorff dimension , we consider general statistics , where the score function vanishes unless the input is close to and where satisfies a weak spatial dependency condition. We give a rate of normal convergence for the rescaled statistics as . When is of class , we obtain weak laws of large numbers and variance asymptotics for these statistics, showing that growth is surface order, that is, of order . We use the general results to deduce variance asymptotics and central limit theorems for statistics arising in stochastic geometry, including Poisson-Voronoi volume and surface area estimators, answering questions in Heveling and Reitzner [Ann. Appl. Probab. 19 (2009) 719-736] and Reitzner, Spodarev and Zaporozhets [Adv. in Appl. Probab. 44 (2012) 938-953]. The general results also yield the limit theory for the number of maximal points in a sample.
Published in at http://dx.doi.org/10.1214/13-AAP992 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)