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On waiting times to populate an environment and a question of statistical inference

Published online by Cambridge University Press:  14 July 2016

F. Thomas Bruss*
Affiliation:
Université Libre de Bruxelles
M. Slavtchova-Bojkova*
Affiliation:
Vrije Universiteit Brussel
*
Postal address: Département de Mathématique et ISRO, Université Libre de Bruxelles, Campus Plaine CP 210, 1050 Bruxelles, Belgium. Email address: tbruss@ulb.ac.be
∗∗Postal address: Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. G. Bontchev str., 1113 Sofia, Bulgaria.

Abstract

The mathematical model we consider here is the classical Bienaymé–Galton–Watson branching process modified with immigration in the state zero.

We study properties of the waiting time to explosion of the supercritical modified process, i.e. that time until all beginning cycles which die out have disappeared. We then derive the expected total progeny of a cycle and show how higher moments can be computed. With a view to applications the main goal is to show that any statistical inference from observed cycle lengths or estimates of total progeny on the fertility rate of the process must be treated with care. As an example we discuss population experiments with trout.

Type
Short Communications
Copyright
Copyright © Applied Probability Trust 1999 

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Footnotes

This work was partly supported by the FWO research network WO-001-96N and grant MM-704/97 of the National Science Foundation.

References

Athreya, K., and Ney, P. (1972). Branching Processes. Springer, Berlin.CrossRefGoogle Scholar
Bhattacharjee, M. C. (1987). The time to extinction of branching processes and log-convexity: I. Prob. Eng. and Inf. Sci. I, 265278.CrossRefGoogle Scholar
Bruss, F. T. (1978). Branching processes with random absorbing processes. J. Appl. Prob. 15, 5464.CrossRefGoogle Scholar
Foster, J. H. (1971). A limit theorem for a branching process with state-dependent immigration. Ann. Math. Statist. 42, 17731776.CrossRefGoogle Scholar
Harris, T. (1989). The Theory of Branching Processes. Dover, New York.Google Scholar
Jagers, P. (1975). Branching Processes with Biological Applications. Wiley-Interscience, London.Google Scholar
Karlin, S. and Tavaré, S. (1982). Detecting particular genotypes in populations under nonrandom mating. Math. Biosci. 59, 5775.CrossRefGoogle Scholar
Pakes, A. G. (1971). A branching process with a state-dependent immigration component. Adv. Appl. Prob. 3, 301314.CrossRefGoogle Scholar
Pakes, A. G. (1975). Some results for non-supercritical Galton–Watson processes with immigration. Math. Biosci. 24, 7192.CrossRefGoogle Scholar
Sevastyanov, B. A., and Zubkov, A. M. (1974). Controlled branching processes. Theory Prob. Appl. 19, 1424.CrossRefGoogle Scholar
Shaked, M., and Shantikumar, J. G. (1987). Characterization of some first passage times using log-concavity and log-convexity as ageing notions. Prob. Eng. and Inf. Sci. I, 279291.CrossRefGoogle Scholar
Yanev, N. M. (1975). Conditions for degeneracy of φ-branching processes with random φ . Prob. Theory Appl. 20, 433440.Google Scholar