arXiv subjects

Günter Last

Publications and source records attributed to Günter Last.

At least 19 recordsLinked to original sources

arXiv2026

Efron type identities for stopping sets and Poisson hulls

We consider a Poisson process $η$ on a general space with intensity measure $λ$ and a stopping set $Z$ depending on $η$. Using in particular the spatial Markov property of $η$, we derive several distributional identities for the restrictions of $η$ and $λ$ to $Z$ and the complement of $Z$. An important special case in Euclidean space is the convex hull of a finite Poisson process. In this case our results generalize classical (and also more recent) identities connecting the number of vertices and the volume of the convex hull. Our results apply to general Poisson hulls and predominantly even to more general random sets which are neither assumed to be bounded nor to be stopping sets.

math.PR↗

arXiv2026

Stochastic analysis for the Dirichlet--Ferguson process

We study a Dirichlet--Ferguson process $ζ$ on a general phase space. First we reprove the chaos expansion from Peccati (2008), providing an explicit formula for the kernel functions. Then we proceed with developing a Malliavin calculus for $ζ$. To this end we introduce a gradient, a divergence and a generator which act as linear operators on random variables or random fields and which are linked by some basic formulas such as integration by parts. While this calculus is strongly motivated by Malliavin calculus for isonormal Gaussian processes and the general Poisson process, the strong dependence properties of $ζ$ require considerably more combinatorial efforts. We apply our theory to identify our generator as the generator of the Fleming--Viot process and to describe the associated Dirichlet form explicitly in terms of the chaos expansion. We also establish the product and chain chain rule for the gradient and an integral representation of the divergence. Finally we give a short direct proof of the Poincaré inequality.

math.PR↗

arXiv2026

A genuine test for hyperuniformity

We introduce a rigorous and sensitive significance test for hyperuniformity that yields reliable results even from a single sample. Our approach is based on a detailed analysis of the empirical Fourier transform of a stationary point process in $\mathbb{R}^d$. For large system sizes, we derive the asymptotic covariances and establish a multivariate central limit theorem (CLT) for these empirical Fourier transforms. Their absolute square value, the scattering intensity, is then used as the standard estimator of the structure factor. The above CLT holds for a preferably large class of point processes, and whenever this is the case, the scattering intensity satisfies a multivariate limit theorem as well. Hence, we can use the likelihood ratio principle to test for hyperuniformity. Remarkably, the asymptotic distribution of the resulting test statistic is universal under the null hypothesis of hyperuniformity. We obtain its explicit form from simulations with very high accuracy. The novel test precisely keeps a nominal significance level for hyperuniform models, and it rejects non-hyperuniform examples with high power even in borderline cases. Moreover, it does so given only a single sample with a practically relevant system size.

math.ST↗

arXiv2026

On strong sharp phase transition in the random connection model

We consider a random connection model (RCM) $ξ$ driven by a Poisson process $η$. We derive exponential moment bounds for an arbitrary cluster, provided that the intensity $t$ of $η$ is below a certain critical intensity $t_T$. The associated subcritical regime is characterized by a finite mean cluster size, uniformly in space. Under an exponential decay assumption on the connection function, we also show that the cluster diameters are exponentially small as well. In the important stationary marked case and under a uniform moment bound on the connection function, we show that $t_T$ coincides with $t_c$, the largest $t$ for which $ξ$ does not percolate. In this case, we also derive some percolation mean field bounds. These findings generalize some of the recent results. Even in the classical unmarked case, our results are more general than what has been previously known. Our proofs are partially based on some stochastic monotonicity properties, which might be of interest in their own right.

math.PR↗

arXiv2025

On the uniqueness of the infinite cluster and the cluster density in the Poisson driven random connection model

We consider a random connection model (RCM) on a general space driven by a Poisson process whose intensity measure is scaled by a parameter $t\ge 0$. We say that the infinite clusters are deletion stable if the removal of a Poisson point cannot split a cluster in two or more infinite clusters. We prove that this stability together with a natural irreducibility assumption implies uniqueness of the infinite cluster. Conversely, if the infinite cluster is unique then this stability property holds. Several criteria for irreducibility will be established. We also study the analytic properties of expectations of functions of clusters as a function of $t$. In particular we show that the position dependent cluster density is differentiable. A significant part of this paper is devoted to the important case of a stationary marked RCM (in Euclidean space), containing the Boolean model with general compact grains and the so-called weighted RCM as special cases. In this case we establish differentiability and a convexity property of the cluster density $κ(t)$. These properties are crucial for our proof of deletion stability of the infinite clusters but are also of interest in their own right. It then follows that an irreducible stationary marked RCM can have at most one infinite cluster. This extends and unifies several results in the literature.

math.PR↗

arXiv2025

Invariant transports of stationary random measures: asymptotic variance, hyperuniformity, and examples

We consider invariant transports of stationary random measures on $\mathbb{R}^d$ and establish natural mixing criteria that guarantee persistence of asymptotic variances. To check our mixing assumptions, which are based on two-point Palm probabilities, we combine factorial moment expansion with stopping set techniques, among others. We complement our results by providing formulas for the Bartlett spectral measure of the destinations. We pay special attention to the case of a vanishing asymptotic variance, known as hyperuniformity. By constructing suitable transports from a hyperuniform source we are able to rigorously establish hyperuniformity for many point processes and random measures. On the other hand, our method can also refute hyperuniformity. For instance, we show that finitely many steps of Lloyd's algorithm or of a random organization model preserve the asymptotic variance if we start from a Poisson process or a point process with exponentially fast decaying correlation. Finally, we define a hyperuniformerer that turns any ergodic point process with finite intensity into a hyperuniform process by randomizing each point within its cell of a fair partition.

math.PR↗

arXiv2024

Boolean models in hyperbolic space

The union of the particles of a stationary Poisson process of compact (convex) sets in Euclidean space is called Boolean model and is a classical topic of stochastic geometry. In this paper, Boolean models in hyperbolic space are considered, where one takes the union of the particles of a stationary Poisson process in the space of compact (convex) subsets of the hyperbolic space. Geometric functionals such as the volume of the intersection of the Boolean model with a compact convex observation window are studied. In particular, the asymptotic behavior for balls with increasing radii as observation windows is investigated. Exact and asymptotic formulas for expectations, variances, and covariances are shown and univariate and multivariate central limit theorems are derived. Compared to the Euclidean framework, some new phenomena can be observed.

math.PR↗

arXiv2024

On the capacity functional of the infinite cluster of a Boolean model

The original 2017 version of this paper, published in Ann. Appl. Probab., 27, 1678--1801, contains a major gap in the proofs. In the subsequent publication in Ann. Appl. Probab., 34, 3370--3374, 2024, we indicated how to fix this. For convenience of the reader, we here update the original paper to incorporate the suggested fix. Consider a Boolean model in $R^d$ with balls of random, bounded radii with distribution $F_0$, centered at the points of a Poisson process of intensity $t>0$. The capacity functional of the infinite cluster $Z_\infty$ is given by $θ_L(t) = P(Z_\infty\cap L \neq \emptyset)$, defined for each compact $L\subset R^d$. We prove for any fixed $L$ and $F_0$ that $θ_L(t)$ is infinitely differentiable in $t$, except at the critical value $t_c$; we give a Margulis-Russo type formula for the derivatives. More generally, allowing the distribution $F_0$ to vary and viewing $θ_L$ as a function of the measure $F:=tF_0$, we show that it is infinitely differentiable in all directions with respect to the measure $F$ in the supercritical region of the cone of positive measures on a bounded interval. We also prove that $θ_L(\cdot)$ grows at least linearly at the critical value. This implies that the critical exponent known as $β$ is at most 1 (if it exists) for this model. Along the way, we extend a result of H.Tanemura (1993), on regularity of the supercritical Boolean model in $d \geq 3$ with fixed-radius balls, to the case with bounded random radii.

math.PR↗

arXiv2024

Poisson hulls

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 an expected linear statistic 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 Hölder function. We show that the estimation error is given by the Kabanov--Skorohod integral with respect to the underlying Poisson process. A crucial ingredient of our approach is a spatial strong Markov property of the underlying Poisson process with respect to the hull. We derive the rate of normal convergence for the estimation error, and illustrate it on an application to estimators of integrals of a Hölder function. We also discuss estimation of higher order symmetric statistics.

math.PR↗

arXiv2023

Lace Expansion and Mean-Field Behavior for the Random Connection Model

We study the random connection model driven by a stationary Poisson process. In the first part of the paper, we derive a lace expansion with remainder term in the continuum and bound the coefficients using a new version of the BK inequality. For our main results, we consider three versions of the connection function $φ$: a finite-variance version (including the Boolean model), a spread-out version, and a long-range version. For sufficiently large dimension (resp., spread-out parameter and $d>6$), we then prove the convergence of the lace expansion, derive the triangle condition, and establish an infra-red bound. From this, mean-field behavior of the model can be deduced. As an example, we show that the critical exponent $γ$ takes its mean-field value $γ=1$ and that the percolation function is continuous.

math.PR↗

arXiv2023

Normal approximation of Kabanov-Skorohod integrals on Poisson spaces

We consider the normal approximation of Kabanov-Skorohod integrals on a general Poisson space. Our bounds are for the Wasserstein and the Kolmogorov distance and involve only difference operators of the integrand of the Kabanov-Skorohod integral. The proofs rely on the Malliavin-Stein method and, in particular, on multiple applications of integration by parts formulae. As examples, we study some linear statistics of point processes that can be constructed by Poisson embeddings and functionals related to Pareto optimal points of a Poisson process.

math.PR↗

arXiv2023

Boolean models

The topic of this survey are geometric functionals of a Boolean model (in Euclidean space) governed by a stationary Poisson process of convex grains. The Boolean model is a fundamental benchmark of stochastic geometry and continuum percolation. Moreover, it is often used to model amorphous connected structures in physics, materials science and biology. Deeper insight into the geometric and probabilistic properties of Boolean models and the dependence on the underlying Poisson process can be gained by considering various geometric functionals of Boolean models. Important examples are the intrinsic volumes and Minkowski tensors. We survey here local and asymptotic density (mean value) formulas as well as second order properties and central limit theorems.

math.PR↗

arXiv2023

Transportation of diffuse random measures on $\mathbb{R}^d$

We consider two jointly stationary and ergodic random measures $ξ$ and $η$ on $\mathbb{R}^d$ with equal finite intensities, assuming $ξ$ to be diffuse. An allocation is a random mapping taking $\mathbb{R}^d$ to $\mathbb{R}^d\cup\{\infty\}$ in a translation invariant way. We construct allocations transporting the diffuse $ξ$ to arbitrary $η$, under the mild condition of existence of an `auxiliary' point process which is needed only in the case when $η$ is diffuse. When that condition does not hold we show by a counterexample that an allocation transporting $ξ$ to $η$ need not exist.

math.PR↗

arXiv2023

Disagreement coupling of Gibbs processes with an application to Poisson approximation

We discuss a thinning and an embedding procedure to construct finite Gibbs processes with a given Papangelou intensity. Extending the approach in Hofer-Temmel (2019) and Hofer-Temmel and Houdebert (2019) we will use this to couple two finite Gibbs processes with different boundary conditions. As one application we will establish Poisson approximation of point processes derived from certain infinite volume Gibbs processes via dependent thinning. As another application we shall discuss empty space probabilities of certain Gibbs processes.

math.PR↗

arXiv2023

Tail processes and tail measures: An approach via Palm calculus

Using an intrinsic approach, we study some properties of random fields which appear as tail fields of regularly varying stationary random fields. The index set is allowed to be a general locally compact Hausdorff Abelian group $\mathbb{G}$. The values are taken in a measurable cone, equipped with a pseudo norm. We first discuss some Palm formulas for the exceedance random measure $ξ$ associated with a stationary (measurable) random field $Y=(Y_s)_{s\in \mathbb{G}}$. It is important to allow the underlying stationary measure to be $σ$-finite. Then we proceed to a random field (defined on a probability space) which is spectrally decomposable, in a sense which is motivated by extreme value theory. We characterize mass-stationarity of the exceedance random measure in terms of a suitable version of the classical Mecke equation. We also show that the associated stationary measure is homogeneous, that is a tail measure. We then proceed with establishing and studying the spectral representation of stationary tail measures and with characterizing a moving shift representation. Finally we discuss anchoring maps and the candidate extremal index.

math.PR↗

arXiv2022

Phase transitions and noise sensitivity on the Poisson space via stopping sets and decision trees

Proofs of sharp phase transition and noise sensitivity in percolation have been significantly simplified by the use of randomized algorithms, via the OSSS inequality (proved by O'Donnell, Saks, Schramm and Servedio (2005)) and the Schramm-Steif inequality for the Fourier-Walsh coefficients of functions defined on the Boolean hypercube. In this article, we prove intrinsic versions of the OSSS and Schramm-Steif inequalities for functionals of a general Poisson process, and apply these new estimates to deduce sufficient conditions - expressed in terms of randomized stopping sets - yielding sharp phase transitions, quantitative noise sensitivity, exceptional times and bounds on critical windows for monotonic Boolean Poisson functions. Our analysis is based on a new general definition of `stopping set', not requiring any topological property for the underlying measurable space, as well as on the new concept of a `continuous-time decision tree', for which we establish several fundamental properties. We apply our findings to the $k$-percolation of the Poisson Boolean model and to the Poisson-based confetti percolation with bounded random grains. In these two models, we reduce the proof of sharp phase transitions for percolation, and of noise sensitivity for crossing events, to the construction of suitable randomized stopping sets and the computation of one-arm probabilities. This enables us to settle some open problem suggested by Ahlberg, Tassion and Texeira (2018) on noise sensitivity of crossing events for the planar Poisson Boolean model and also planar Confetti percolation model. Further, we also prove that critical probability is $1/2$ in certain planar confetti percolation models. A special case of this result was conjectured by Benjamini and Schramm (1998) and proved by Müller (2017). Other special cases were proven by Hirsch (2015) and Ghosh and Roy (2018).

math.PR↗

arXiv2021

On the uniqueness of Gibbs distributions with a non-negative and subcritical pair potential

We prove that the distribution of a Gibbs process with non-negative pair potential is uniquely determined as soon as an associated Poisson-driven random connection model (RCM) does not percolate. Our proof combines disagreement coupling in continuum with a coupling of a Gibbs process and a RCM. The improvement over previous uniqueness results is illustrated both in theory and simulations.

math.PR↗

arXiv2020

Decorrelation of a class of Gibbs particle processes and asymptotic properties of U-statistics

We study a stationary Gibbs particle process with deterministically bounded particles on Euclidean space defined in terms of an activity parameter and non-negative interaction potentials of finite range. Using disagreement percolation we prove exponential decay of the correlation functions, provided a dominating Boolean model is subcritical. We also prove this property for the weighted moments of a U-statistic of the process. Under the assumption of a suitable lower bound on the variance, this implies a central limit theorem for such U-statistics of the Gibbs particle process. A byproduct of our approach is a new uniqueness result for Gibbs particle processes.

math.PR↗