Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-05T04:26:21.190Z Has data issue: false hasContentIssue false

Shot noise Cox processes

Published online by Cambridge University Press:  01 July 2016

Jesper Møller*
Affiliation:
Aalborg University
*
Postal address: Department of Mathematical Sciences, Aalborg University, Fredrik Bajers Vej 7G, DK-9220 Aalborg, Denmark. Email address: [email protected]

Abstract

Shot noise Cox processes constitute a large class of Cox and Poisson cluster processes in ℝd, including Neyman-Scott, Poisson-gamma and shot noise G Cox processes. It is demonstrated that, due to the structure of such models, a number of useful and general results can easily be established. The focus is on the probabilistic aspects with a view to statistical applications, particularly results for summary statistics, reduced Palm distributions, simulation with or without edge effects, conditional simulation of the intensity function and local and spatial Markov properties.

Type
Stochastic Geometry and Statistical Applications
Copyright
Copyright © Applied Probability Trust 2003 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Ambartzumian, R. V. (1966). On an equation for stationary point processes. Akad. Nauk. Armjanskoi SSR Dokl. 42, 141147 (in Russian).Google Scholar
[2] Baddeley, A. and Möller, J. (1989). Nearest-neighbour Markov point processes and random sets. Internat. Statist. Rev. 2, 89121.Google Scholar
[3] Baddeley, A. J. and van Lieshout, M. N. M. (1993). Stochastic geometry models in high-level vision. In Statistics and Images. 1. (suppl. J. Appl. Statist. 20), eds Mardia, K. V. and Kanji, G. K., Carfax Publishing, Abingdon, pp. 235256.Google Scholar
[4] Baddeley, A., Möller, J. and Waagepetersen, R. (2000). Non- and semi-parametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica 54, 329350.Google Scholar
[5] Baddeley, A. J., van Lieshout, M. N. M. and Möller, J. (1996). Markov properties of cluster processes. Adv. Appl. Prob. 28, 346355.CrossRefGoogle Scholar
[6] Benes, V., Bodlak, K., Möller, J. and Waagepetersen, R. P. (2002). Bayesian analysis of log Gaussian Cox process models for disease mapping. Tech. Rep. R-02–2001, Department of Mathematical Sciences, Aalborg University.Google Scholar
[7] Best, N. G., Ickstadt, K. and Wolpert, R. (2000). Spatial Poisson regression for health and exposure data measured at disparate resolutions. J. Amer. Statist. Assoc. 95, 10761088.CrossRefGoogle Scholar
[8] Brix, A. (1999). Generalized gamma measures and shot-noise Cox processes. Adv. Appl. Prob. 31, 929953.Google Scholar
[9] Brix, A. and Chadœuf, J. (2000). Spatio-temporal modeling of weeds and shot-noise G Cox processes. Submitted.Google Scholar
[10] Brix, A. and Diggle, P. J. (2001). Spatio-temporal prediction for log-Gaussian Cox processes. J. R. Statist. Soc. B 63, 823841.CrossRefGoogle Scholar
[11] Brix, A. and Kendall, W. S. (2002). Simulation of cluster point processes without edge effects. Adv. Appl. Prob. 34, 267280.Google Scholar
[12] Brix, A. and Möller, J. (2001). Space–time multitype log Gaussian Cox processes with a view to modelling weed data. Scand. J. Statist. 28, 471488.CrossRefGoogle Scholar
[13] Chin, Y. C. and Baddeley, A. (2000). Markov interacting component processes. Adv. Appl. Prob. 32, 597619.Google Scholar
[14] Daley, D. J. and Vere-Jones, D. (1988). An Introduction to the Theory of Point Processes. Springer, New York.Google Scholar
[15] Diggle, P. J. (1983). Statistical Analysis of Spatial Point Patterns. Academic Press, London.Google Scholar
[16] Georgii, H.-O. (1976). Canonical and grand canonical Gibbs states for continuum systems. Commun. Math. Phys. 48, 3151.Google Scholar
[17] Geyer, C. J. (1999). Likelihood inference for spatial point processes. In Stochastic Geometry: Likelihood and Computation (Monogr. Statist. Appl. Prob. 80), eds Barndorff-Nielsen, O. E., Kendall, W. S. and van Lieshout, M. N. M., Chapman and Hall/CRC, London, pp. 79140.Google Scholar
[18] Geyer, C. J. and Möller, J. (1994). Simulation procedures and likelihood inference for spatial point processes. Scand. J. Statist. 21, 359373.Google Scholar
[19] Heikkinen, J. and Arjas, E. (1998). Non-parametric Bayesian estimation of a spatial Poisson intensity. Scand. J. Statist. 25, 435450.Google Scholar
[20] Kallenberg, O. (1984). An informal guide to the theory of conditioning in point processes. Internat. Statist. Rev. 52, 151164.CrossRefGoogle Scholar
[21] Kendall, W. S. (1990). A spatial Markov property for nearest-neighbour Markov point processes. J. Appl. Prob. 28, 767778.Google Scholar
[22] Kerstan, J. and Matthes, K. (1964). Verallgemeinerung eines Satzes von Sliwnjak. Rev. Roumaine Math. Pures Appl. IX, 811829.Google Scholar
[23] Lawson, A. B. (1993). Discussion contribution. J. R. Statist. Soc. B 55, 6162.Google Scholar
[24] Matérn, B., (1960). Spatial Variation (Meddelanden Statens Skogforskningsinst. 49). Statens Skogsforskningsinstitut, Stockholm. Second edition: Lecture Notes Statist. 36, Springer, Berlin, 1986.Google Scholar
[25] Mecke, J. (1967). Stationäre zufällige Maße auf lokalkompakten Abelschen Gruppen. Z. Wahrscheinlichkeitsth. 9, 3658.Google Scholar
[26] Meester, R. and Roy, R. (1996). Continuum Percolation. Cambridge University Press, New York.Google Scholar
[27] Meyn, S. P. and Tweedie, R. L. (1993). Markov Chains and Stochastic Stability. Springer, London.Google Scholar
[28] Möller, J., (1994). Discussion: N. L. Hjort and H. Omre ‘Topics in spatial statistics’. Scand. J. Statist. 21, 346349.Google Scholar
[29] Möller, J., (1999). Markov chain Monte Carlo and spatial point processes. In Stochastic Geometry: Likelihood and Computation (Monogr. Statist. Appl. Prob. 80), eds Barndorff-Nielsen, O. E., Kendall, W. S. and van Lieshout, M. N. M., Chapman and Hall/CRC, London, pp. 141172.Google Scholar
[30] Möller, J., (2003). A comparison of spatial point process models in epidemiological applications. In Highly Structured Stochastic Systems, eds Green, P. J., Hjort, N. L. and Richardson, S., Oxford University Press, pp. 264268.Google Scholar
[31] Möller, J. and Waagepetersen, R. P. (1998). Markov connected component fields. Adv. Appl. Prob. 30, 135.Google Scholar
[32] Möller, J. and Waagepetersen, R. P. (2002). Statistical inference for Cox processes. In Spatial Cluster Modelling, eds Lawson, A. B. and Denison, D., Chapman and Hall/CRC, Boca Raton, FL, pp. 3760.Google Scholar
[33] Möller, J. and Waagepetersen, R. P. (2003). Statistical Inference and Simulation for Spatial Point Processes (Monog. Statist. Appl. Prob. 100). Chapman and Hall/CRC, Boca Raton, FL.Google Scholar
[34] Möller, J., Syversveen, A. R. and Waagepetersen, R. P. (1998). Log Gaussian Cox processes. Scand. J. Statist. 25, 451482.CrossRefGoogle Scholar
[35] Neyman, J. and Scott, E. L. (1958). Statistical approach to problems of cosmology. J. R. Statist. Soc. B 20, 143.Google Scholar
[36] Nguyen, X. X. and Zessin, H. (1979). Integral and differential characterizations of Gibbs processes. Math. Nachr. 88, 105115.Google Scholar
[37] Ripley, B. D. (1976). The second-order analysis of stationary point processes. J. Appl. Prob. 13, 255266.Google Scholar
[38] Ripley, B. D. (1977). Modelling spatial patterns (with discussion). J. R. Statist. Soc. B 39, 172212.Google Scholar
[39] Ripley, B. D. (1987). Stochastic Simulation. John Wiley, New York.CrossRefGoogle Scholar
[40] Ripley, B. D. and Kelly, F. P. (1977). Markov point processes. J. London Math. Soc. 15, 188192.CrossRefGoogle Scholar
[41] Ruelle, D. (1969). Statistical Mechanics: Rigorous Results. W. A. Benjamin, Reading, MA.Google Scholar
[42] Santaló, L., (1976). Integral Geometry and Geometric Probability. Addison-Wesley, Reading, MA.Google Scholar
[43] Stoyan, D. and Stoyan, H. (1994). Fractals, Random Shapes and Point Fields. John Wiley, Chichester.Google Scholar
[44] Stoyan, D., Kendall, W. S. and Mecke, J. (1995). Stochastic Geometry and Its Applications, 2nd edn. John Wiley, Chichester.Google Scholar
[45] Thomas, M. (1949). A generalization of Poisson's binomial limit for use in ecology. Biometrika 36, 1825.Google Scholar
[46] Van Lieshout, M. N. M. and Baddeley, A. J. (1995). Markov chain Monte Carlo methods for clustering of image features. In Proc. 5th IEE Internat. Conf. Image Processing and its Applications (IEE Conf. Publication 410), IEE Press, London, pp. 241245.Google Scholar
[47] Van Lieshout, M. N. M. and Baddeley, A. J. (1996). A nonparametric measure of spatial interaction in point patterns. Statistica Neerlandica 50, 344361.Google Scholar
[48] Van Lieshout, M. N. M. and Baddeley, A. J. (2002). Extrapolating and interpolating spatial patterns. In Spatial Cluster Modelling, eds. Lawson, A. B. and Denison, D., Chapman and Hall, Boca Raton, FL, pp. 6186.Google Scholar
[49] Wolpert, R. L. and Ickstadt, K. (1998). Poisson/gamma random field models for spatial statistics. Biometrika 85, 251267.Google Scholar