Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-10T14:49:24.346Z Has data issue: false hasContentIssue false

Weak convergence of an autoregressive process used in modeling population growth

Published online by Cambridge University Press:  14 July 2016

W. G. Cumberland*
Affiliation:
University of California at Los Angeles
Z. M. Sykes*
Affiliation:
The Johns Hopkins University
*
Postal address: School of Public Health, University of California, Los Angeles, CA 90024, U.S.A.
∗∗ Postal address: Department of Population Dynamics, The Johns Hopkins University, Baltimore, MD 21205, U.S.A.

Abstract

Under simple limiting conditions a first-order autoregressive process is shown to converge weakly to an Ornstein-Uhlenbeck process. The result is discussed in the context of modeling vital rates for biological populations in random environments.

Type
Short Communications
Copyright
Copyright © Applied Probability Trust 1982 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Research supported by a training grant (5-TO1-HN109), by a research career program award (5-KO4-Hd70404), and by a research grant (1-RO1-HD8959), all from the National Institute of Child Health and Human Development.

References

Billingsley, P. (1968) Convergence of Probability Measures. Wiley, New York.Google Scholar
Capocelli, R. M. and Ricciardi, L. M. (1974) A diffusion model for population growth in a random environment. Theoret. Popn Biol. 5, 2841.CrossRefGoogle Scholar
Cumberland, W. G. and Rohde, C. A. (1977) A multivariate model for growth of populations. Theoret. Popn Biol. 11, 127139.CrossRefGoogle ScholarPubMed
Feller, W. (1971) An Introduction to Probability Theory and Its Applications, Vol. II, 2nd edn. Wiley, New York.Google Scholar
Kendall, D. G. (1949) Stochastic processes and population growth. J. R. Statist. Soc. B 11, 230264.Google Scholar
Lee, R. D. (1974) Forecasting births in post transition populations. J. Amer. Statist. Assoc. 69, 607617.CrossRefGoogle Scholar
Leslie, P. H. (1968) Some further notes on the use of matrices in population mathematics. Biometrika 35, 213245.CrossRefGoogle Scholar
Levins, R. (1969) The effect of random variation on diffusion types of population growth. Proc. Nat. Acad. Sci. USA 62, 10611065.CrossRefGoogle Scholar
Lewontin, R. C. and Cohen, D. (1969) On population growth in randomly varying environments. Proc. Nat. Acad. Sci. USA 62, 10561060.CrossRefGoogle Scholar
Lotka, A. J. and Sharpe, F. R. (1911) A problem in age distribution. Phil. Mag. 21, 435438.Google Scholar
Polansky, P. (1979) Invariant distributions for multi-population models in random environments. Theoret. Popn Biol. 16, 2534.CrossRefGoogle ScholarPubMed
Pollard, J. H. (1969) Continuous-time and discrete-time models of population growth. J. R. Statist. Soc. A 132, 8088.Google Scholar
Saboia, J. L. M. (1977) Autoregressive integrated moving average (ARIMA) models for birth forecasting. J. Amer. Statist. Assoc. 72, 264270.CrossRefGoogle Scholar
Smith, W. L. and Wilkinson, W. E. (1969) On branching processes in random environments. Ann. Math. Statist. 40, 814827.CrossRefGoogle Scholar
Sweden: Central Bureau Of Statistics (1955) Historical Statistics of Sweden: I. Population 1720–1950. Statens Reproduktionsanstalt, Stockholm.Google Scholar
Tuckwell, H. C. (1974) Diffusion models of population growth. Theoret. Popn Biol. 5, 345357.CrossRefGoogle ScholarPubMed