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We consider a collection of Euclidean random balls in ${Bbb R}^d$ generated by a determinantal point process inducing interaction into the balls. We study this model at a macros-copic level obtained by a zooming-out and three different regimes --Gaussian, Poissonian and stable-- are exhibited as in the Poissonian model without interaction. This shows that the macroscopic behaviour erases the interactions induced by the determinantal point process.
We consider weighted random balls in $real^d$ distributed according to a random Poisson measure with heavy-tailed intensity and study the asymptotic behaviour of the total weight of some configurations in $real^d$. This procedure amounts to be very r
We consider a generalization of the weighted random ball model. The model is driven by a random Poisson measure with a product heavy tailed intensity measure. Such a model typically represents the transmission of a network of stations with a fading e
For $dge 3$ we construct a new coupling of the trace left by a random walk on a large $d$-dimensional discrete torus with the random interlacements on $mathbb Z^d$. This coupling has the advantage of working up to macroscopic subsets of the torus. As
It is generally argued that the solution to a stochastic PDE with multiplicative noise---such as $dot{u}=frac12 u+uxi$, where $xi$ denotes space-time white noise---routinely produces exceptionally-large peaks that are macroscopically multifractal. Se
Accurate estimation of tail probabilities of projections of high-dimensional probability measures is of relevance in high-dimensional statistics and asymptotic geometric analysis. For fixed $p in (1,infty)$, let $(X^{(n,p)})$ and $(theta^n)$ be indep