• Corpus ID: 254246816

Poisson hulls

@inproceedings{Last2022PoissonH,
  title={Poisson hulls},
  author={Gunter Last and Ilya Molchanov},
  year={2022}
}
: We introduce a hull operator on Poisson point processes, the easiest example being the convex hull of the support of a point process in Euclidean space. Assuming that the intensity measure of the process is known on the set generated by the hull operator, we discuss estimation of the expected linear statistics built on the Poisson process. In special cases, our general scheme yields an estimator of the volume of a convex body or an estimator of an integral of a H¨older function. We show that… 

References

SHOWING 1-10 OF 19 REFERENCES

Multivariate Normal Approximation for Functionals of Random Polytopes

Consider the random polytope that is given by the convex hull of a Poisson point process on a smooth convex body in $$\mathbb {R}^d$$ R d . We prove central limit theorems for continuous motion

Unbiased estimation of the volume of a convex body

Normal approximation for stabilizing functionals

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

Stochastic analysis for Poisson processes

This survey is a preliminary version of a chapter of the forthcoming book [21]. The paper develops some basic theory for the stochastic analysis of Poisson process on a general σ-finite measure

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

Central limit theorems for $U$-statistics of Poisson point processes are shown, with explicit bounds for the Wasserstein distance to a Gaussian random variable and the length of a random geometric graph are investigated.

POISSON HYPERPLANE PROCESSES AND APPROXIMATION OF CONVEX BODIES

A natural model for the approximation of a convex body $K$ in $\mathbb{R}^d$ by random polytopes is obtained as follows. Take a stationary Poisson hyperplane process in the space, and consider the

Methods for Estimation of Convex Sets

This work focuses on the estimation of convex sets under the Nykodim and the Hausdorff metrics, which require different techniques and, quite surprisingly, lead to very different results, in particular in density support estimation.

Invariance of Poisson measures under random transformations

We prove that Poisson measures are invariant under (random) intensity preserving transformations whose finite difference gradient satisfies a cyclic vanishing condition. The proof relies on moment

Functional estimation and hypothesis testing in nonparametric boundary models

Consider a Poisson point process with unknown support boundary curve $g$, which forms a prototype of an irregular statistical model. We address the problem of estimating non-linear functionals of the

Cones generated by random points on half-spheres and convex hulls of Poisson point processes

Let $$U_1,U_2,\ldots $$U1,U2,… be random points sampled uniformly and independently from the d-dimensional upper half-sphere. We show that, as $$n\rightarrow \infty $$n→∞, the f-vector of the