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Estimation variances for Poisson processes of compact sets

Published online by Cambridge University Press:  01 July 2016

Tomáš Mrkvička*
Affiliation:
Charles University, Prague
*
Postal address: Faculty of Mathematics and Physics, Charles University, Sokolovská 83, 18675, Praha 8, Czech Republic. Email address: mrkvicka@karlin.mff.cuni.cz

Abstract

A complete and sufficient statistic is found for various stationary Poisson processes of compact sets with known primary grain. In the particular case of a segment process, the uniformly best unbiased estimator for the length density is the number of segments hitting the sampling window divided by a certain constant and multiplied by the mean segment length.

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

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References

[1] Baddeley, A. J. and Cruz-Orive, L. M. (1995). The Rao–Blackwell theorem in stereology and some counterexamples. Adv. Appl. Prob. 27, 219.Google Scholar
[2] Chadoeuf, J., Senoussi, R. and Yao, J. F. (2000). Parametric estimation of a boolean segment process with stochastic restoration estimation. J. Comput. Graph. Statist. 9, 390402.Google Scholar
[3] Daley, D. J. and Vere-Jones, D. (1988). An Introduction to the Theory of Point Processes. Springer, New York.Google Scholar
[4] Lehmann, E. L. (1991). Theory of Point Estimation. Wadsworth and Brooks, California.Google Scholar
[5] Mrkvicka, T., (1999). Estimation variances for Poisson process of compact sets. , Charles University (in Czech).Google Scholar
[6] Matheron, G. (1975). Random Sets and Integral Geometry. John Wiley, New York.Google Scholar
[7] Schladitz, K. (2000). Estimation of the intensity of stationary flat processes. Adv. Appl. Prob. 32, 114139.CrossRefGoogle Scholar
[8] Stoyan, D., Kendall, W. S. and Mecke, J. (1985). Stochastic Geometry and Its Applications, 2nd edn. John Wiley, Chichester.Google Scholar
[9] Van Zwet, E. W. (1999). Likelihood devices in spatial statistics. Proefschrift, Faculteit Wiskunde en Informatica, Universiteit Utrecht.Google Scholar
[10] Weil, W. and Wieacker, J. A. (1993). Stochastic geometry. In Handbook of Convex Geometry, Vol. B, eds Gruber, P. M. and Wills, J. M., North-Holland, Amsterdam, pp. 13911438.Google Scholar