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The expected time until absorption when absorption is not certain

Published online by Cambridge University Press:  14 July 2016

D. M. Walker*
Affiliation:
The University of Queensland
*
Postal address: Department of Mathematics, The University of Queensland, QLD 4072, Australia. Email address: dmw@maths.uq.edu.au.

Abstract

This paper considers continuous-time Markov chains whose state space consists of an irreducible class, 𝒞, and an absorbing state which is accessible from 𝒞. The purpose is to provide a way to determine the expected time to absorption conditional on such time being finite, in the case where absorption occurs with probability less than 1. The results are illustrated by applications to the general birth and death process and the linear birth, death and catastrophe process.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1998 

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References

Brockwell, P. J. (1985). The extinction time of a birth, death and catastrophe process and of a related diffusion model. Adv. Appl. Prob. 17, 4252.Google Scholar
Brockwell, P. J., Gani, J., and Resnick, S. I. (1982). Birth, immigration and catastrophe processes. Adv. Appl. Prob. 14, 709731.CrossRefGoogle Scholar
Jacka, S. D., and Roberts, G. O. (1997). On strong forms of weak convergence. Stoch. Proc. Appl. 67, 4153.CrossRefGoogle Scholar
Karlin, S., and Taylor, H. M. (1993). An Introduction to Stochastic Modeling, Academic Press, New York.Google Scholar
Reuter, G. E. H. (1957). Denumerable Markov processes and the associate contraction semigroups on l 1 . Acta Math. 97, 146.Google Scholar
Reuter, G. E. H. (1961). Competition process. Proc. Fourth Berkeley Symp. Math. Statist. Prob. 2, 421430.Google Scholar
Seneta, E. (1981). Non-Negative Matrices and Markov Chains, 2nd edn. Springer, New York.CrossRefGoogle Scholar
Tweedie, R. L. (1981). Criteria for ergodicity, exponential ergodicity, and strong ergodicity of Markov processes. J. Appl. Prob. 18, 122130.Google Scholar
Waugh, W. A. O'N. (1958). Conditioned Markov processes. Biometrika 45, 241249.Google Scholar