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Simulation of cluster point processes without edge effects

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Brix, Anders and Kendall, Wilfrid S.. (2002) Simulation of cluster point processes without edge effects. Advances in Applied Probability, Vol.34 (No.2). pp. 267-280. ISSN 0001-8678

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Official URL: http://dx.doi.org/10.1239/aap/1025131217

Abstract

The usual direct method of simulation for cluster processes requires the generation of the parent point process over a region larger than the actual observation window, since we have to allow for all possible parents giving rise to observed daughter points, and some of these parents may fall outwith the observation window. When there is no a priori bound on the distance between parent and child then we have to take care to control approximations arising from edge effects. In this paper, we present a simulation method which requires simulation only of those parent points actually giving rise to observed daughter points, thus avoiding edge effect approximation. The idea is to replace the cluster distribution by one which is conditioned to plant at least one daughter point in the observation window, and to modify the parent process to have an inhomogeneous intensity exactly balancing the effect of the conditioning. We furthermore show how the method extends to cases involving infinitely many potential parents, for example gamma-Poisson processes and shot-noise G-Cox processes, allowing us to avoid approximation due to truncation of the parent process.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Cluster analysis
Journal or Publication Title: Advances in Applied Probability
Publisher: Applied Probability Trust
ISSN: 0001-8678
Date: June 2002
Volume: Vol.34
Number: No.2
Number of Pages: 14
Page Range: pp. 267-280
Identification Number: 10.1239/aap/1025131217
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Engineering and Physical Sciences Research Council (EPSRC), European Union (EU), European Science Foundation (ESF)
Grant number: GR/L56831 (EPSRC), GR/M75785 (EPSRC), ERB-FMRXCT96-0095 (EU)
References: [1] J. van den Berg and J.E. Steif. On the existence and non-existence of finitary codings for a class of random fields. The Annals of Probability, 27(3):1501–1522, 1999. [2] L. Bondesson. On simulation from infinitely divisible distributions. Advances in Applied Probability, 14:855–869, 1982. [3] A. Brix. Generalized gamma measures and shot-noise Cox processes. Advances in Applied Probability, 31:929–953, 1999. [4] A. Brix and J. Chadoeuf. Spatio-temporal modelling of weeds by shot-noise G Cox processes. Biometrical Journal, 44:83–99, 2002. [5] A. Brix and J. Møller. Space-time multi type log Gaussian Cox processes with a view to modelling weeds. Scandinavian Journal of Statistics, 28:471– 488, 2001. [6] D.R. Cox. Some statistical methods related with series of events (with discussion). Journal of the Royal Statistical Society (Series B: Methodological), 17:129–157, 1955. [7] D.J. Daley and D. Vere-Jones. An introduction to the theory of point processes. Springer-Verlag, New York, 1988. [8] O. H¨aggstr¨om and J.E. Steif. Propp-Wilson algorithms and finitary codings for high noise Markov random fields. Combin. Probab. Computing, 9:425– 439, 2000. [9] W.S. Kendall. Perfect simulation for spatial point processes. In Bulletin ISI, 51st session proceedings, Istanbul (August 1997), volume 3, pages 163– 166, Voorburg, 1997. International Statistical Institute. [10] J. Neyman. On a class of contagious distributions, applicable in entomology and bacteriology. Annals of Mathematical Statistics, 10(1):35–57, 1939. [11] J. Neyman and E.L. Scott. Statistical approach to problems of cosmology. Journal of the Royal Statistical Society (Series B: Methodological), 20:1–43, 1958. [12] A. Penttinen, D. Stoyan, and H. Henttonen. Marked point processes in forest statistics. Forest.Sci., 38:806–824, 1992. [13] D. Stoyan, W.S. Kendall, and J. Mecke. Stochastic geometry and its applications. John Wiley & Sons, New York, 1995. [14] R.L. Wolpert and K. Ickstadt. Poisson/Gamma random field models for spatial statistics. Biometrika, 85:251–267, 1998.
URI: http://wrap.warwick.ac.uk/id/eprint/10717

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