Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-26T02:06:53.328Z Has data issue: false hasContentIssue false

On Comparison of Clustering Properties of Point Processes

Published online by Cambridge University Press:  22 February 2016

Bartłomiej Błaszczyszyn*
Affiliation:
INRIA/ENS
D. Yogeshwaran*
Affiliation:
Technion, Israel Institute of Technology
*
Postal address: INRIA/ENS, 23 Av. d'Italie, 75214 Paris Cedex 13, France. Email address: [email protected]
∗∗ Postal address: Department of Electrical Engineering, Technion, Israel Institute of Technology, Haifa, 32000, Israel. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In this paper, we propose a new comparison tool for spatial homogeneity of point processes, based on the joint examination of void probabilities and factorial moment measures. We prove that determinantal and permanental processes, as well as, more generally, negatively and positively associated point processes are comparable in this sense to the Poisson point process of the same mean measure. We provide some motivating results on percolation and coverage processes, and preview further ones on other stochastic geometric models, such as minimal spanning forests, Lilypond growth models, and random simplicial complexes, showing that the new tool is relevant for a systemic approach to the study of macroscopic properties of non-Poisson point processes. This new comparison is also implied by the directionally convex ordering of point processes, which has already been shown to be relevant to the comparison of the spatial homogeneity of point processes. For this latter ordering, using a notion of lattice perturbation, we provide a large monotone spectrum of comparable point processes, ranging from periodic grids to Cox processes, and encompassing Poisson point processes as well. They are intended to serve as a platform for further theoretical and numerical studies of clustering, as well as simple models of random point patterns to be used in applications where neither complete regularity nor the total independence property are realistic assumptions.

Type
Research Article
Copyright
© Applied Probability Trust 

References

Aldous, D. and Steele, J. Μ. (1992). Asymptotics for Euclidean minimal spanning trees on random points. Prob. Theory Relat. Fields 92, 247-258.Google Scholar
Alexander, K. S. (1995). Percolation and minimal spanning forests in infinite graphs. Ann. Prob. 23, 87-104.CrossRefGoogle Scholar
Baccelli, F., Błaszczyszyn, B. and Haji-Mirsadeghi, Μ. O. (2011). Optimal paths on the space-time SINR random graph. Adv. Appl. Prob. 43, 131-150.Google Scholar
Błaszczyszyn, B. and Yogeshwaran, D. (2009). Directionally convex ordering of random measures, shot-noise fields, and some applications to wireless communications. Adv. Appl. Prob. 41, 623-646.Google Scholar
Błaszczyszyn, B. and Yogeshwaran, D. (2013). Clustering and percolation of point processes. Electron. J. Prob. 18, 20pp.Google Scholar
Błaszczyszyn, B. and Yogeshwaran, D. (2011). Clustering, percolation and directionally convex ordering of point processes. Preprint. Available at http://uk.arxiv.org/abs/1105.4293v1.Google Scholar
Burton, R. and Waymire, E. (1985). Scaling limits for associated random measures. Ann. Prob. 13, 1267-1278.Google Scholar
Coupechoux, E. and Lelarge, Μ., (2011). Impact of clustering on diffusions and contagions in random networks. In Proc. NetGCOOP 2011, Paris, 1-7.Google Scholar
Daley, D. J. and Last, G. (2005). Descending chains, the lilypond model, and mutual-nearest-neighbour matching. Adv. Appl. Prob. 37, 604-628.Google Scholar
Georgii, H.-O. and Yoo, H. J. (2005). Conditional intensity and Gibbsianness of determinantal point processes. J. Statist. Phys. 118, 55-84.CrossRefGoogle Scholar
Goldman, A. (2010). The Palm measure and the Voronoi tessellation for the Ginibre process. Ann. Appl. Prob. 20, 90-128.Google Scholar
Hirsch, C., Neuhauser, D. and Schmidt, V. (2013). Connectivity of random geometric graphs related to minimal spanning forests. Adv. Appl. Prob. 45, 20-36.CrossRefGoogle Scholar
Hough, J. B., Krishnapur, Μ., Peres, Y. and Virág, B. (2006). Determinantal processes and independence. Prob. Surveys 3, 206-229.CrossRefGoogle Scholar
Hough, J. B., Krishnapur, Μ., Peres, Y. and Virág, B. (2009). Zeros of Gaussian Analytic Functions and Determinantal Point Processes (Univ. Lecture Ser. 51). American Mathematical Society, Providence, RI.Google Scholar
Joag-Dev, K. and Proschan, F. (1983). Negative association of random variables with applications. Ann. Statist. 11, 286-295.Google Scholar
Kahle, Μ. (2011). Random geometric complexes. Discrete Comput. Geom. 45, 553-573.Google Scholar
Kallenberg, O. (1983). Random Measures, 3rd edn. Akademie-Verlag, Berlin.Google Scholar
Lyons, R. (2003). Determinantal probability measures. Publ. Math. Inst. Hautes Études Sci. 98, 167-212.Google Scholar
Marchand, R. (2002). Strict inequalities for the time constant in first passage percolation. Ann. Appl. Prob. 12, 1001-1038.Google Scholar
Marcus, Μ. (1964). The Hadamard theorem for permanents. Proc. Amer. Math. Soc. 15, 967-973.CrossRefGoogle Scholar
Marshall, A. W., Olkin, I. and Arnold, B. C. (2011). Inequalities: Theory of Majorization and Its Applications, 2nd edn. Springer, New York.Google Scholar
Meester, L. E. and Shanthikumar, J. G. (1993). Regularity of stochastic processes: a theory based on directional convexity. Prob. Eng. Inf. Sci. 7, 343-360.Google Scholar
Meester, R. and Roy, R. (1996). Continuum Percolation (Camb. Tracts Math. 119). Cambridge University Press.Google Scholar
Miyoshi, N. and Shirai, T. (2014). A cellular network model with Ginibre configurated base stations. To appear in Adv. Appl. Prob. Google Scholar
Müller, A. and Stoyan, D. (2002). Comparison Methods for Stochastic Models and Risks. John Wiley, Chichester.Google Scholar
Pemantle, R. (2000). Towards a theory of negative dependence. J. Math. Phys. 41, 1371-1390.Google Scholar
Penrose, Μ. D. (1996). Continuum percolation and Euclidean minimal spanning trees in high dimensions. Ann. Appl. Prob. 6, 528-544.Google Scholar
Penrose, Μ. (2003). Random Geometric Graphs. Oxford University Press.Google Scholar
Peres, Y and Virág, B. (2005). Zeros of the i.i.d. Gaussian power series: a conformally invariant determinantal process. Acta Math. 194, 1-35.Google Scholar
Schulte, Μ. and Thäle, C. (2012). The scaling limit of Poisson-driven order statistics with applications in geometric probability. Stoch. Process. Appl. 122, 4096-4120.Google Scholar
Sodin, Μ. and Tsirelson, B. (2004). Random complex zeroes. I. Asymptotic normality. Israel J. Math. 144, 125-149.Google Scholar
Sodin, Μ. and Tsirelson, B. (2006). Random complex zeroes. II. Perturbed lattice. Israel J. Math. 152, 105-124.CrossRefGoogle Scholar
Van den Berg, J. and Kesten, H. (1993). Inequalities for the time constant in first-passage percolation. Ann. Appl. Prob. 3, 56-80.Google Scholar
Whitt, W. (1985). Uniform conditional variability ordering of probability distributions. J. Appl. Prob. 22, 619-633.CrossRefGoogle Scholar
Yan, Μ. (2014). Extension of convex function. To appear in J. Convex Anal. Google Scholar
Yogeshwaran, D. (2010). Stochastic geometric networks: connectivity and comparison. Doctoral Thesis. Université Pierre et Marie Curie.Google Scholar
Yogeshwaran, D. and Adler, R. J. (2012). On the topology of random complexes built over stationary point processes. Preprint. Available at http://uk.arxiv.org/abs/1211.0061.Google Scholar