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Mixed Cox processes, with an application to accident statistics

Published online by Cambridge University Press:  14 July 2016

Abstract

A doubly stochastic switching model previously used by the author to check against doubly stochastic ‘white-noise' models is generalized in the case of Poisson processes to any distribution of fluctuating intensity rates. The resulting Cox process is used in place of a simple Poisson process as the basis of a mixed model, which is then fitted to some data on automobile accidents previously analysed by Seal (1980) in terms of a mixed Poisson process. It is shown that there is just significant evidence in favour of some additional heterogeneity ‘within' each year for individual drivers; the nature of the data analysed (yearly time intervals) does not, however, allow discrimination between heterogeneity which is definitely attributable to a common fluctuating environment, and that which is in effect independent for each driver.

Type
Part 6—Allied Stochastic Processes
Copyright
Copyright © 1986 Applied Probability Trust 

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References

Barndorff-Nielsen, O. and Yeo, G. F. (1969) Negative binomial processes. J. Appl. Prob. 6, 633647.Google Scholar
Bartlett, M. S. (1982) Some stochastic models in biology. Utilitas Math. 21A, 291309.Google Scholar
Bartlett, M. S. (1983) On doubly stochastic processes. In Probability, Statistics and Analysis , ed. Kingman, J. F. C. and Reuter, G. E. H., Cambridge University Press, 2445.Google Scholar
Seal, H. L. (1980) Mixed Poisson processes and risk theory (Notes of a lecture course, University of Lausanne).Google Scholar