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Bounds for the Probability Generating Functional of a Gibbs Point Process

Published online by Cambridge University Press:  22 February 2016

Kaspar Stucki*
Affiliation:
University of Bern
Dominic Schuhmacher*
Affiliation:
University of Bern
*
Current address: Institute for Mathematical Stochastics, University of Göttingen, Goldschmidtstrasse 7, 37077 Göttingen, Germany
Current address: Institute for Mathematical Stochastics, University of Göttingen, Goldschmidtstrasse 7, 37077 Göttingen, Germany
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Abstract

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We derive explicit lower and upper bounds for the probability generating functional of a stationary locally stable Gibbs point process, which can be applied to summary statistics such as the F function. For pairwise interaction processes we obtain further estimates for the G and K functions, the intensity, and higher-order correlation functions. The proof of the main result is based on Stein's method for Poisson point process approximation.

MSC classification

Type
Research Article
Copyright
© Applied Probability Trust 

References

Abramowitz, M. and Stegun, I. A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables (Nat. Bureau Standards Appl. Math. Ser. 55). US Government Printing Office, Washington, DC.Google Scholar
Baddeley, A. and Nair, G. (2012). Approximating the moments of a spatial point process. Stat 1, 1830.CrossRefGoogle Scholar
Baddeley, A. and Nair, G. (2012). Fast approximation of the intensity of Gibbs point processes. Electron. J. Statist. 6, 11551169.CrossRefGoogle Scholar
Baddeley, A. and Turner, R. (2005). Spatstat: an R package for analyzing spatial point patterns. J. Statist. Software 12, 42pp.CrossRefGoogle Scholar
Barbour, A. D. (1988). Stein's method and Poisson process convergence. In A Celebration of Applied Probability (J. Appl. Prob. Spec. Vol. 25A), Applied Probability Trust, Sheffield, pp. 175184.Google Scholar
Barbour, A. D. and Brown, T. C. (1992). Stein's method and point process approximation. Stoch. Process. Appl. 43, 931.CrossRefGoogle Scholar
Daley, D. J. and Vere-Jones, D. (2008). An Introduction to the Theory of Point Processes, Vol. II, General Theory and Structure, 2nd edn. Springer, New York.Google Scholar
Kendall, W. S. and Møller, J. (2000). Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes. Adv. Appl. Prob. 32, 844865.CrossRefGoogle Scholar
Mase, S. (1990). Mean characteristics of Gibbsian point processes. Ann. Inst. Statist. Math. 42, 203220.CrossRefGoogle Scholar
Møller, J. and Waagepetersen, R. P. (2004). Statistical Inference and Simulation for Spatial Point Processes (Monogr. Statist. Appl. Prob. 100). Chapman & Hall/CRC, Boca Raton, FL.Google Scholar
Møller, J. and Waagepetersen, R. P. (2007). Modern statistics for spatial point processes. Scand. J. Statist. 34, 643684.CrossRefGoogle Scholar
Nguyen, X.-X. and Zessin, H. (1979). Integral and differential characterizations of the Gibbs process. Math. Nachr. 88, 105115.Google Scholar
Ruelle, D. (1969). Statistical Mechanics: Rigorous Results. W. A. Benjamin. New York.Google Scholar
Schuhmacher, D. and Stucki, K. (2013). Gibbs point process approximation: total variation bound using Stein's method. To appear in Ann. Prob. Preprint available at http://uk.arxiv.org/abs/1207.3096.Google Scholar
Stoyan, D. (2012). Personal communication.Google Scholar
Stoyan, D., Kendall, W. S. and Mecke, J. (1995). Stochastic Geometry and Its Applications, 2nd edn. John Wiley, Chichester.Google Scholar
Xia, A. (2005). Stein's method and Poisson process approximation. In An Introduction to Stein's Method, Singapore University Press, pp. 115181.CrossRefGoogle Scholar