Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-10T21:09:31.888Z Has data issue: false hasContentIssue false

Conditionally Independent Increment Point Processes

Published online by Cambridge University Press:  14 July 2016

Ricardo Vélez Ibarrola*
Affiliation:
Universidad Nacional de Educación a Distancia
Tomás Prieto-Rumeau*
Affiliation:
Universidad Nacional de Educación a Distancia
*
Postal address: Departamento de Estadística, Universidad Nacional de Educación a Distancia (UNED), Calle Senda del Rey 9, 28040 Madrid, Spain.
Postal address: Departamento de Estadística, Universidad Nacional de Educación a Distancia (UNED), Calle Senda del Rey 9, 28040 Madrid, Spain.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In this paper we introduce conditionally independent increment point processes, that is, processes that are conditionally independent inside and outside a bounded set A given N(A), the number of points in A. We show that these point processes can be characterized by means of the avoidance function of a multinomial ‘support process’, the solution of a suitably defined linear system of equations, and, finally, the infinitesimal matrix of a continuous-time Markov chain.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2011 

References

[1] Chow, Y. S. and Teicher, H. (1988). Probability Theory, 2nd edn. Springer, New York.CrossRefGoogle Scholar
[2] Chung, K. L. (1967). Markov Chains with Stationary Transition Probabilities. Springer, New York.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] Diggle, P. J. (2003). Statistical Analysis of Spatial Point Patterns, 2nd edn. Edward Arnold, London.Google Scholar
[5] Kallenberg, O. (1983). Random Measures, 3rd edn. Academic Press, London.Google Scholar
[6] Karr, A. F. (1991). Point Processes and Their Statistical Inference, 2nd edn. Marcel Dekker, New York.Google Scholar
[7] Kingman, J. F. C. (1993). Poisson Processes. Oxford Univerity Press, New York.Google Scholar
[8] Moyal, J. E. (1962). The general theory of stochastic population processes. Acta Math. 108, 131.Google Scholar
[9] Van Lieshout, M. N. M. (2000). Markov Point Processes and Their Applications. Imperial College Press, London.Google Scholar