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On Laslett's transform for the Boolean model

Published online by Cambridge University Press:  01 July 2016

A. D. Barbour*
Affiliation:
Universität Zürich
V. Schmidt*
Affiliation:
Universität Ulm
*
Postal address: Department of Applied Mathematics, University of Zurich, CH-8057 Zurich, Switzerland.
∗∗ Postal address: Institute of Stochastics, Faculty of Mathematics and Economics, University of Ulm, D-89069 Ulm, Germany. Email address: [email protected]

Abstract

Consider the Boolean model in ℝ2, where the germs form a homogeneous Poisson point process with intensity λ and the grains are convex compact random sets. It is known (see, e.g., Cressie (1993, Section 9.5.3)) that Laslett's rule transforms the exposed tangent points of the Boolean model into a homogeneous Poisson process with the same intensity. In the present paper, we give a simple proof of this result, which is based on a martingale argument. We also consider the cumulative process of uncovered area in a vertical strip and show that a (linear) Poisson process with intensity λ can be embedded in it.

Type
Stochastic Geometry and Statistical Applications
Copyright
Copyright © Applied Probability Trust 2001 

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References

Cressie, N. A. C. (1993). Statistics for Spatial Data, Revised edn. John Wiley, New York.Google Scholar
Kallenberg, O. (1997). Foundations of Modern Probability. Springer, New York.Google Scholar
Molchanov, I. S. (1995). Statistics of the Boolean model: from the estimation of means to the estimation of distributions. Adv. Appl. Prob. 27, 6386.Google Scholar
Molchanov, I. S. (1997). Statistics of the Boolean Model for Practitioners and Mathematicians. John Wiley, Chichester.Google Scholar
Schneider, R. and Weil, W. (2000). Stochastische Geometrie. Teubner, Stuttgart.Google Scholar
Stoyan, D., Kendall, W. S. and Mecke, J. (1995). Stochastic Geometry and its Application, 2nd edn. John Wiley, Chichester.Google Scholar