Reach of repulsion for determinantal point processes in high dimensions

Goldman (2010) proved that the distribution of a stationary determinantal point process (DPP) Φ can be coupled with its reduced Palm version Φ0,! such that there exists a point process η where Φ=Φ0,!∪η in distribution and Φ0,!∩η=∅. The points of η characterize the repulsive nature of a typical point of Φ. In this paper we use the first-moment measure of η to study the repulsive behavior of DPPs in high dimensions. We show that many families of DPPs have the property that the total number of points in η converges in probability to 0 as the space dimension n→∞. We also prove that for some DPPs, there exists an R∗ such that the decay of the first-moment measure of η is slowest in a small annulus around the sphere of radius √nR∗. This R∗ can be interpreted as the asymptotic reach of repulsion of the DPP. Examples of classes of DPP models exhibiting this behavior are presented and an application to high-dimensional Boolean models is given.

On the capacity functional of excursion sets of Gaussian random fields on ℝ2

When a random field (Xt,t∈ℝ2) is thresholded on a given level u, the excursion set is given by its indicator 1[u, ∞)(Xt). The purpose of this work is to study functionals (as established in stochastic geometry) of these random excursion sets as, e.g. the capacity functional as well as the second moment measure of the boundary length. It extends results obtained for the one-dimensional case to the two-dimensional case, with tools borrowed from crossings theory, in particular, Rice methods, and from integral and stochastic geometry.

SECOND MOMENT MEASURE AND K-FUNCTION FOR PLANAR STIT TESSELLATIONS

For STIT tessellations – stationary tessellations that are stable under the operation iteration of tessellations – the second-ordermeasure of the edge system is studied. A result is that this measure coincides with that one of a Boolean segment process. In the isotropic case an explicit formula for the pair-correlation function is given. An estimator for the covariance function of the edge length measure is derived and adapted to digitized images of tessellations. For m pixels of an image the algorithm is of complexity O(mlogm).