45 research outputs found
Shape-Driven Nested Markov Tessellations
A new and rather broad class of stationary (i.e. stochastically translation
invariant) random tessellations of the -dimensional Euclidean space is
introduced, which are called shape-driven nested Markov tessellations. Locally,
these tessellations are constructed by means of a spatio-temporal random
recursive split dynamics governed by a family of Markovian split kernel,
generalizing thereby the -- by now classical -- construction of iteration
stable random tessellations. By providing an explicit global construction of
the tessellations, it is shown that under suitable assumptions on the split
kernels (shape-driven), there exists a unique time-consistent whole-space
tessellation-valued Markov process of stationary random tessellations
compatible with the given split kernels. Beside the existence and uniqueness
result, the typical cell and some aspects of the first-order geometry of these
tessellations are in the focus of our discussion
Typical Geometry, Second-Order Properties and Central Limit Theory for Iteration Stable Tessellations
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
cell-splitting and crack-formation processes. The purpose of this paper is to
describe large scale asymptotic geometry of STIT tessellations in
and more generally that of non-stationary iteration infinitely
divisible tessellations. We study several aspects of the typical first-order
geometry of such tessellations resorting to martingale techniques as providing
a direct link between the typical characteristics of STIT tessellations and
those of suitable mixtures of Poisson hyperplane tessellations. Further, we
also consider second-order properties of STIT and iteration infinitely
divisible tessellations, such as the variance of the total surface area of cell
boundaries inside a convex observation window. Our techniques, relying on
martingale theory and tools from integral geometry, allow us to give explicit
and asymptotic formulae. Based on these results, we establish a functional
central limit theorem for the length/surface increment processes induced by
STIT tessellations. We conclude a central limit theorem for total edge
length/facet surface, with normal limit distribution in the planar case and
non-normal ones in all higher dimensions.Comment: 51 page
Poisson approximation of the length spectrum of random surfaces
Multivariate Poisson approximation of the length spectrum of random surfaces
is studied by means of the Chen-Stein method. This approach delivers simple and
explicit error bounds in Poisson limit theorems. They are used to prove that
Poisson approximation applies to curves of length up to order
with being the genus of the surface.Comment: 22 pages, 2 figures. To appear in Indiana Univ. Math.
A four moments theorem for Gamma limits on a Poisson chaos
This paper deals with sequences of random variables belonging to a fixed
chaos of order generated by a Poisson random measure on a Polish space. The
problem is investigated whether convergence of the third and fourth moment of
such a suitably normalized sequence to the third and fourth moment of a centred
Gamma law implies convergence in distribution of the involved random variables.
A positive answer is obtained for and . The proof of this four
moments theorem is based on a number of new estimates for contraction norms.
Applications concern homogeneous sums and -statistics on the Poisson space
Geometry of iteration stable tessellations: Connection with Poisson hyperplanes
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
cell-splitting and crack-formation processes. We provide in this paper a
fundamental link between typical characteristics of STIT tessellations and
those of suitable mixtures of Poisson hyperplane tessellations using martingale
techniques and general theory of piecewise deterministic Markov processes
(PDMPs). As applications, new mean values and new distributional results for
the STIT model are obtained.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ424 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm). arXiv admin
note: text overlap with arXiv:1001.099
The scaling limit of Poisson-driven order statistics with applications in geometric probability
Let be a Poisson point process of intensity on some state
space \Y and be a non-negative symmetric function on \Y^k for some
. Applying to all -tuples of distinct points of
generates a point process on the positive real-half axis. The scaling
limit of as tends to infinity is shown to be a Poisson point
process with explicitly known intensity measure. From this, a limit theorem for
the the -th smallest point of is concluded. This is strengthened by
providing a rate of convergence. The technical background includes Wiener-It\^o
chaos decompositions and the Malliavin calculus of variations on the Poisson
space as well as the Chen-Stein method for Poisson approximation. The general
result is accompanied by a number of examples from geometric probability and
stochastic geometry, such as Poisson -flats, Poisson random polytopes,
random geometric graphs and random simplices. They are obtained by combining
the general limit theorem with tools from convex and integral geometry