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Related papers: Limit theorems for iteration stable tessellations

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Since the seminal work of Mecke, Nagel and Weiss, the iteration stable (STIT) tessellations have attracted considerable interest in stochastic geometry as a natural and flexible yet analytically tractable model for hierarchical spatial…

Probability · Mathematics 2015-03-13 Tomasz Schreiber , Christoph Thaele

This paper deals with iteration stable (STIT) tessellations, and, more generally, with a certain class of tessellations that are infinitely divisible with respect to iteration. They form a new, rich and flexible class of spatio-temporal…

Probability · Mathematics 2013-03-04 Tomasz Schreiber , Christoph Thaele

Since the seminal work by Nagel and Weiss, the iteration stable (STIT) tessellations have attracted considerable interest in stochastic geometry as a natural and flexible, yet analytically tractable model for hierarchical spatial…

Probability · Mathematics 2014-12-25 Tomasz Schreiber , Christoph Thaele

Three-dimensional random tessellations that are stable under iteration (STIT tessellations) are considered. They arise as a result of subsequent cell division, which implies that their cells are not face-to-face. The edges of the…

Probability · Mathematics 2012-09-26 Christoph Thaele , Viola Weiss , Werner Nagel

A Gilbert tessellation arises by letting linear segments (cracks) in the plane unfold in time with constant speed, starting from a homogeneous Poisson point process of germs in randomly chosen directions. Whenever a growing edge hits an…

Probability · Mathematics 2010-05-04 Tomasz Schreiber , Natalia Soja

We consider homogeneous STIT tessellations in the $\ell$-dimensional Euclidean space ${\mathbb R}^\ell$. Based on results for the spatial $\beta$-mixing coefficient an upper bound for the variance of additive functionals of tessellations is…

Probability · Mathematics 2016-09-06 Servet Martínez , Werner Nagel

Stationary and isotropic iteration stable random tessellations are considered, which can be constructed by a random process of cell division. The collection of maximal polytopes at a fixed time $t$ within a convex window $W\subset{\Bbb…

Probability · Mathematics 2011-04-05 Tomasz Schreiber , Christoph Thaele

The point process of vertices of an iteration infinitely divisible or more specifically of an iteration stable random tessellation in the Euclidean plane is considered. We explicitly determine its covariance measure and its pair-correlation…

Probability · Mathematics 2011-04-05 Tomasz Schreiber , Christoph Thaele

The lower-dimensional maximal polytopes associated with an iteration stable (STIT) tessellation in $\RR^d$ are considered. They arise in the spatio-temporal construction process of such a tessellation as intersections of $(d-1)$-dimensional…

Probability · Mathematics 2012-09-04 Nguyen Ngoc Linh , Christoph Thaele

The stable under iterated tessellation (STIT) process is a stochastic process that produces a recursive partition of space with cut directions drawn independently from a distribution over the sphere. The case of random axis-aligned cuts is…

Machine Learning · Statistics 2021-09-15 Eliza O'Reilly , Ngoc Tran

We observe stationary random tessellations $X=\{\Xi_n\}_{n\ge1}$ in $\mathbb{R}^d$ through a convex sampling window $W$ that expands unboundedly and we determine the total $(k-1)$-volume of those $(k-1)$-dimensional manifold processes which…

Probability · Mathematics 2007-09-14 Lothar Heinrich , Hendrik Schmidt , Volker Schmidt

This paper presents the asymptotic theory for nondegenerate $U$-statistics of high frequency observations of continuous It\^{o} semimartingales. We prove uniform convergence in probability and show a functional stable central limit theorem…

Probability · Mathematics 2014-09-10 Mark Podolskij , Christian Schmidt , Johanna F. Ziegel

Various mixing properties of $\beta$-, $\beta'$- and Gaussian Delaunay tessellations in $\mathbb{R}^{d-1}$ are studied. It is shown that these tessellation models are absolutely regular, or $\beta$-mixing. In the $\beta$- and the Gaussian…

Probability · Mathematics 2021-08-24 Anna Gusakova , Zakhar Kabluchko , Christoph Thäle

We give an overview of the recent asymptotic results on the geometry of excursion sets of stationary random fields. Namely, we cover a number of limit theorems of central type for the volume of excursions of stationary (quasi--, positively…

Probability · Mathematics 2013-07-24 Evgeny Spodarev

A random recursive cell splitting scheme of the $2$-dimensional unit sphere is considered, which is the spherical analogue of the STIT tessellation process from Euclidean stochastic geometry. First-order moments are computed for a large…

Probability · Mathematics 2017-11-06 Christian Deuß , Julia Hörrmann , Christoph Thaele

In this article we establish new central limit theorems for Ruppert-Polyak averaged stochastic gradient descent schemes. Compared to previous work we do not assume that convergence occurs to an isolated attractor but instead allow…

Probability · Mathematics 2019-12-20 Steffen Dereich , Sebastian Kassing

We consider Betti numbers of the excursion of a smooth Euclidean Gaussian field restricted to a rectangular window, in the asymptotics where the window grows to R^d . With motivations coming from Topological Data Analysis, we derive a…

Probability · Mathematics 2025-12-16 Christian Hirsch , Raphaël Lachièze-Rey

The stratified resampling mechanism is one of the resampling schemes commonly used in the resampling steps of particle filters. In the present paper, we prove a central limit theorem for this mechanism under the assumption that the initial…

Probability · Mathematics 2023-08-07 Roberta Flenghi , Benjamin Jourdain

A new and rather broad class of stationary (i.e. stochastically translation invariant) random tessellations of the $d$-dimensional Euclidean space is introduced, which are called shape-driven nested Markov tessellations. Locally, these…

Probability · Mathematics 2013-09-16 Tomasz Schreiber , Christoph Thaele

We study the asymptotic shape of the trajectory of the stochastic gradient descent algorithm applied to a convex objective function. Under mild regularity assumptions, we prove a functional central limit theorem for the properly rescaled…

Machine Learning · Statistics 2026-02-18 Kessang Flamand , Victor-Emmanuel Brunel
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