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A continuous-time population model with poisson recruitment

Published online by Cambridge University Press:  14 July 2016

Sally I. McClean
Sally I. McClean*
Affiliation:
New University of Ulster
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Abstract

A continuous-time model of a multigrade system is developed, which includes Poisson arrivals, interaction between grades and a leaving process. It therefore constitutes a continuous-time analogue of Pollard's hierarchical population model with Poisson recruitment. An expression is found for the first and second moments of grade size at any time. A general formulation of the joint probability generating function of the numbers in each grade is given, and the limiting distribution of grade size is shown to be Poisson.

Keywords

MULTIGRADE SYSTEMCONTINUOUS-TIME STOCHASTIC MODELMANPOWER PLANNING MODELPOPULATION MODELMARKOV PROCESSES
Type
Short Communications
Information
Journal of Applied Probability , Volume 13 , Issue 2 , June 1976 , pp. 348 - 354
Copyright
Copyright © Applied Probability Trust 1976 

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References

[1] Bartholomew, D. J. (1973) Stochastic Models for Social Processes , 2nd edn. Wiley, London.Google Scholar
[2] Gani, J. (1963) Formulae for projecting enrolments and degrees awarded in universities J. R. Statist. Soc. A 126, 400–409.Google Scholar
[3] Herbst, P. G. (1963) Organizational commitment: a decision model. Acta Sociologica 7, 34–45.CrossRefGoogle Scholar
[4] McClean, S. I. (1976) The two stage model of personnel behaviour J. R. Statist. Soc. A 139.CrossRefGoogle Scholar
[5] Pollard, J. H. (1967) Hierarchical population models with Poisson recruitment. J. Appl. Prob. 4, 209–213.CrossRefGoogle Scholar
[6] Young, A. and Almond, G. (1961) Predicting distributions of staff. Comp. J. 3, 246–250.CrossRefGoogle Scholar