Stability for random measures, point processes and discrete semigroups.
Journal article, 2011

Discrete stability extends the classical notion of stability to random elements in discrete spaces by defining a scaling operation in a randomised way: an integer is transformed into the corresponding binomial distribution. Similarly defining the scaling operation as thinning of counting measures we characterise the corresponding discrete stability property of point processes. It is shown that these processes are exactly Cox (doubly stochastic Poisson) processes with strictly stable random intensity measures. We give spectral and LePage representations for general strictly stable random measures without assuming their independent scattering. As a consequence, spectral representations are obtained for the probability generating functional and void probabilities of discrete stable processes. An alternative cluster representation for such processes is also derived using the so-called Sibuya point processes, which constitute a new family of purely random point processes. The obtained results are then applied to explore stable random elements in discrete semigroups, where the scaling is defined by means of thinning of a point process on the basis of the semigroup. Particular examples include discrete stable vectors that generalise discrete stable random variables and the family of natural numbers with the multiplication operation, where the primes form the basis.

cluster process

random measure

spectral measure

discrete semigroup

Sibuya distribution


strict stability

discrete stability

Cox process


Y. Davydov

University of Lille

I. Molchanov

University of Bern

Sergey Zuev

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg


1350-7265 (ISSN)

Vol. 17 3 1015-1043

Subject Categories

Probability Theory and Statistics



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