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The compound Poisson immigration process subject to binomial catastrophes

Published online by Cambridge University Press:  14 July 2016

Antonis Economou*
Affiliation:
University of Athens
*
Postal address: Department of Mathematics, University of Athens, Panepistemiopolis, Athens 15784, Greece. Email address: [email protected]

Abstract

Recently, several authors have studied the transient and the equilibrium behaviour of stochastic population processes with total catastrophes. These models are reasonable for modelling populations that are exposed to extreme disastrous phenomena. However, under mild disastrous conditions, the appropriate model is a stochastic process subject to binomial catastrophes. In the present paper we consider a special such model in which a population evolves according to a compound Poisson process and catastrophes occur according to a renewal process. Every individual of the population survives after a catastrophe with probability p, independently of anything else, i.e. the population size is reduced according to a binomial distribution. We study the equilibrium distribution of this process and we derive an algorithmic procedure for its approximate computation. Bounds on the error of this approximation are also included.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 2004 

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References

Adelson, R. M. (1966). Compound Poisson distributions. Operat. Res. Quart. 17, 7375.Google Scholar
Artalejo, J. R. (2000). G-networks: a versatile approach for work removal in queueing networks. Europ. J. Operat. Res. 126, 233249.Google Scholar
Bartoszynski, R., Buhler, W. J., Chan, W., and Pearl, D. K. (1989). Population processes under the influence of disasters occurring independently of population size. J. Math. Biol. 27, 179190.Google Scholar
Brémaud, P. (1999). Markov Chains: Gibbs Fields, Monte Carlo Simulation and Queues (Text. Appl. Math. 31). Springer, New York.Google Scholar
Brockwell, P. J. (1986). The extinction time of a general birth and death process with catastrophes. J. Appl. Prob. 23, 851858.Google Scholar
Brockwell, P. J., Gani, J., and Resnick, S. I. (1982). Birth, immigration and catastrophe processes. Adv. Appl. Prob. 14, 709731.Google Scholar
Chao, X., and Zheng, Y. (2003). Transient and equilibrium analysis of an immigration birth–death process with total catastrophes. Prob. Eng. Inf. Sci. 17, 83106.Google Scholar
Chao, X., Pinedo, M., and Miyazawa, M. (1999). Queueing Networks: Customers, Signals and Product Form Solutions. John Wiley, New York.Google Scholar
Conway, J. B. (1973). Functions of One Complex Variable (Graduate Texts Math. 11). Springer, New York.Google Scholar
Economou, A., and Fakinos, D. (2003). A continuous-time Markov chain under the influence of a regulating point process and applications in stochastic models with catastrophes. Europ. J. Operat. Res. 149, 625640.Google Scholar
Hanson, F. B., and Tuckwell, H. C. (1997). Population growth with randomly distributed jumps. J. Math. Biol. 36, 169187.Google Scholar
Hordijk, A., and Tijms, H. C. (1976). A simple proof of the equivalence of the limiting distributions of the continuous time and the embedded process of the queue size in the M/G/1 queue. Statistica Neerlandica 30, 97100.Google Scholar
Kyriakidis, E. G. (1994). Stationary probabilities for a simple immigration–birth–death process under the influence of total catastrophes. Statist. Prob. Lett. 20, 239240.Google Scholar
Kyriakidis, E. G. (2001). The transient probabilities of the simple immigration–catastrophe process. Math. Scientist 26, 5658.Google Scholar
Lee, C. (2000). The density of the extinction probability of a time homogeneous linear birth and death process under the influence of randomly occuring disasters. Math. Biosci. 164, 93102.Google Scholar
Neuts, M. F. (1981). Matrix-Geometric Solutions in Stochastic Models: An Algorithmic Approach. Johns Hopkins University Press, Baltimore, MD. (Reprinted: Dover, New York, 1984.)Google Scholar
Neuts, M. F. (1989). Structured Stochastic Matrices of M/G/1 Type and Their Applications. Marcel Dekker, New York.Google Scholar
Neuts, M. F. (1994). An interesting random walk on the nonnegative integers. J. Appl. Prob. 31, 4858.Google Scholar
Rachev, S. T. (1991). Probability Metrics and the Stability of Stochastic Models. John Wiley, Chichester.Google Scholar
Stirzaker, D. (2001). Disasters. Math. Scientist 26, 5962.Google Scholar
Swift, R. J. (2000). A simple immigration–catastrophe process. Math. Scientist 25, 3236.Google Scholar
Tijms, H. C. (1994). Stochastic Models: An Algorithmic Approach. John Wiley, Chichester.Google Scholar
Wolff, R. W. (1982). Poisson arrivals see time averages. Operat. Res. 30, 223231.CrossRefGoogle Scholar