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A bivariate counting process

Published online by Cambridge University Press:  14 July 2016

Ray Watson*
Affiliation:
University of Melbourne
Paul Yip*
Affiliation:
University of Hong Kong
*
Postal address: Department of Statistics, University of Melbourne, Parkville, VIC 3052, Australia.
∗∗ Postal address: Department of Statistics, University of Hong Kong, Pokfulam Road, Hong Kong.

Abstract

We consider a bivariate Markov counting process with transition probabilities having a particular structure, which includes a number of useful population processes. Using a suitable random time-scale transformation, we derive some probability statements about the process and some asymptotic results. These asymptotic results are also derived using martingale methods. Further, it is shown that these methods and results can be used for inference on the rate parameters for the process. The general epidemic model and the square law conflict model are used as illustrative examples.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1993 

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References

Aalen, O. O. (1978) Non-parametric inference for a family of counting processes. Ann. Statist. 6, 701726.Google Scholar
Andersen, P. K. and Borgan, Ø. (1985) Counting process models for life history data: A review. Scand. J. Statist. 12, 97158.Google Scholar
Bailey, N. T. J. (1975) The Mathematical Theory of Infectious Diseases. Griffin, London.Google Scholar
Gye, R. and Lewis, T. (1976) Lanchester's equations: mathematics and the art of war. A historical survey and some new results. Math. Scientist. 1, 107119.Google Scholar
Watson, R. (1981) A useful random time scale transformation for the standard epidemic model. J. Appl. Prob. 17, 324332.Google Scholar
Whittle, P. (1955) The outcome of a stochastic epidemic – a note on Bailey's paper. Biometrika 42, 116122.Google Scholar
Yip, P. and Watson, R. (1991) An inference procedure for conflict models. Stoch. Proc. Appl. 37, 161171.Google Scholar