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The Galton-Watson process revisited: some martingale relationships and applications

Published online by Cambridge University Press:  14 July 2016

James D. Lynch*
Affiliation:
University of South Carolina
*
Postal address: University of South Carolina, Center for Reliability and Quality Sciences, Department of Statistics, University of South Carolina, Columbia, SC 29208, USA. Email address: lynch@stat.sc.edu

Abstract

A martingale is used to study extinction probabilities of the Galton-Watson process using a stopping time argument. This same martingale defines a martingale function in its argument s; consequently, its derivative is also a martingale. The argument s can be classified as regular or irregular and this classification dictates very different behavior of the Galton-Watson process. For example, it is shown that irregularity of a point s is equivalent to the derivative martingale sequence at s being closable, (i.e., it has limit which, when attached to the original sequence, the martingale structure is retained). It is also shown that for irregular points the limit of the derivative is the derivative of the limit, and two different types of norming constants for the asymptotics of the Galton-Watson process are asymptotically equivalent only for irregular points.

Type
Research Papers
Copyright
Copyright © by the Applied Probability Trust 2000 

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Footnotes

Supported by NSF grant DMS 9503104 and DMS 9877107.

References

Athreya, K. B., and Ney, P. E. (1972). Branching Processes. Springer, Berlin.CrossRefGoogle Scholar
Chung, K. L. (1974). A Course in Probability Theory. Academic Press, San Diego.Google Scholar
Biggins, J. D., and D'Souza, J. C. (1993). The supercritical Galton–Watson process in varying environments–-Seneta–Heyde norming. Stoch. Proc. Appl. 48, 237249.CrossRefGoogle Scholar
Cohn, H. (1988). Limit behavior for stochastic monotonicity and applications. Adv. Appl. Prob. 20, 331347.CrossRefGoogle Scholar
Darling, D. A. (1970). The Galton–Watson process with infinite mean. J. Appl. Prob. 7, 455456.CrossRefGoogle Scholar
D'Souza, J. C., and Biggins, J. D. (1992). The supercritical Galton–Watson process in varying environments. Stoch. Proc. Appl. 42, 3947.CrossRefGoogle Scholar
D'Souza, J. C. (1994). The rate of growth of the Galton–Watson process in varying environments. Adv. Appl. Prob. 26, 698714.CrossRefGoogle Scholar
Grey, D. R. (1977). Almost sure convergence in Markov branching processes with infinite mean. J. Appl. Prob. 14, 702716.CrossRefGoogle Scholar
Heyde, C. C. (1970). Extension of a result of Seneta for the supercritical Galton–Watson process. Ann. Math. Statist. 41, 739742.CrossRefGoogle Scholar
Neveu, J. (1975). Discrete Parameter Martingales. North-Holland, Amsterdam.Google Scholar
Sankaranarayan, R. (1989). Branching Processes and its Estimation Theory. John Wiley, New York.Google Scholar
Schuh, H. J., and Barbour, A. D. (1977). On the asymptotic behavior of branching processes with infinite mean. Adv. Appl. Prob. 9, 681723.CrossRefGoogle Scholar
Seneta, E. (1968). On recent theorems concerning the supercritical Galton–Watson Process. Ann. Math. Statist. 41, 20982102.CrossRefGoogle Scholar
Seneta, E. (1969). Functional equations and the Galton–Watson process. Adv. Appl. Prob. 1, 142.CrossRefGoogle Scholar