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Poisson convergence for point processes on the plane

Published online by Cambridge University Press:  09 April 2009

B. Gail Ivanoff
Affiliation:
Department of MathematicsUniversity of OttawaOttawa, Ontario K1N 9B4, Canada
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Abstract

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A compensator is defined for a point process in two dimensions. It is shown that a Poisson process is characterized by a continuous deterministic compensator. Sufficient conditions are given for convergence in distribution of a sequence of two-dimensional point processes in the Skorokhod topology to a Poisson process when the corresponding sequence of compensators converges pointwise in probability to a continuous deterministic function.

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 1985

References

Aldous, D. J. (1978), ‘Stopping times and tightness’, Ann. Probability 6, 335340.Google Scholar
Billingsley, P. (1979), Probability and measure (Wiley, New York).Google Scholar
Brennan, M. D. (1979), ‘Planar semimartingales’, J. Multivariate Anal. 9, 465486.Google Scholar
Brown, T. C. (1978), ‘A martingale approach to the Poisson convergence of simple point processes’, Ann. Probability 6, 615628.Google Scholar
Brown, T. C. (1981), ‘Compensators and Cox convergence’, Math. Proc. Cambridge Philos. Soc. 90, 305319.CrossRefGoogle Scholar
Dozzi, M. (1981), ‘On the decomposition and integration of two-parameter stochastic processes’, pp. 162171CrossRefGoogle Scholar
(Lecture Notes in Math. 863, Springer, New York).Google Scholar
Ivanoff, B. G. (1980), ‘The function space D([0, ∞)q, E)’, Canad. J. Statist. 9, 179191.Google Scholar
Ivanoff, B. G. (1983), ‘Stopping times and tightness in two dimensions’, pp. 4665 (Technical Report Series of the Laboratory for Research in Statistics and Probability, No. 1).Google Scholar
Jacod, J. (1975), ‘Multivariate point processes: predictable projection, Radon-Nikodym derivatives, representation of martingales’, Z. Wahrscheinlichkeitshteorie und Verw. Gabiete 31, 235253.Google Scholar
Kabanov, Yu. M., Liptser, R. Sh. and Shiryayev, A. N. (1980), ‘Some limit theorems for simple point processes (a martingale approach)’, Stochastics 3, 203216.Google Scholar
Kallenberg, O. (1978), ‘On conditional intensities of point processes’, Z. Wahrscheinlichkeitstheorie und Verw. Gabiete 41, 205220.CrossRefGoogle Scholar
Liptser, R. S., and Shiryayev, A. N. (1978), Statistics of random processes, Vol. 2 (Springer, New York).Google Scholar
McLeish, D. L. (1978), ‘An extended martingale invariance principle’, Ann. Probability 6, 144150.Google Scholar
Merzbach, E. and Zakai, M. (1980), ‘Predictable and dual predictable projections of two-parameter stochastic processes’, Z. Wahrscheinlichkeitshteorie und Verw. Gebiete 53, 263269.CrossRefGoogle Scholar
Meyer, P. A. (1981), ‘Théorie élémentaire des processus à deux indices’, pp. 139 (Lecture Notes in Math. 863, Springer, New York).Google Scholar
Walsh, J. B. (1979), ‘Convergence and regularity of multi-parameter strong martingales’, Z. Wahrscheinlichkeitshteorie und Verw. Gebiete 46, 177192.CrossRefGoogle Scholar
Wong, E. and Zakai, M. (1976), ‘Weak martingales and stochastic integrals in the plane’, Ann. Probability 4, 570586.Google Scholar
Yeh, J. (1981), ‘Stopping times and an extension of stochastic integrals in the plane’, J. Multivariate Anal. 11, 334345.Google Scholar