# Clustering and percolation of point processes

1 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : We show that simple, stationary point processes of a given intensity on $\mR^d$, having void probabilities and factorial moment measures smaller than those of a homogeneous Poisson point process of the same intensity, admit uniformly non-degenerate lower and upper bounds on the critical radius $r_c$ for the percolation of their continuum percolation models. Examples are negatively associated point processes and, more specifically, determinantal point processes. More generally, we show that point processes $dcx$ smaller than a homogeneous Poisson point processes (for example perturbed lattices) exhibit phase transitions in certain percolation models based on the level-sets of additive shot-noise fields of these point processes. Examples of such models are $k$-percolation and SINR-percolation models. Our study is motivated by heuristics and numerical evidences obtained for perturbed lattices, indicating that point processes exhibiting stronger clustering of points have larger $r_c$. Since the suitability of the $dcx$ ordering of point processes for comparison of clustering tendencies was known, it was tempting to conjecture that $r_c$ is increasing in the $dcx$ order. However the conjecture is not true in full generality as one can construct a Cox point process with degenerate critical radius $r_c=0$, that is $dcx$ larger than a given homogeneous Poisson point process.
Type de document :
Article dans une revue
Electronic Journal of Probability, Institute of Mathematical Statistics (IMS), 2013, 18, pp.art. 72:1-20. 〈10.1214/EJP.v18-2468〉
Domaine :

https://hal.inria.fr/hal-00651491
Contributeur : Bartlomiej Blaszczyszyn <>
Soumis le : mardi 13 décembre 2011 - 16:47:04
Dernière modification le : vendredi 25 mai 2018 - 12:02:06

### Citation

Bartlomiej Blaszczyszyn, Dhandapani Yogeshwaran. Clustering and percolation of point processes. Electronic Journal of Probability, Institute of Mathematical Statistics (IMS), 2013, 18, pp.art. 72:1-20. 〈10.1214/EJP.v18-2468〉. 〈hal-00651491〉

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