Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-26T16:40:30.676Z Has data issue: false hasContentIssue false

ON THE OUTCOME OF A CASCADING FAILURE MODEL

Published online by Cambridge University Press:  01 June 2006

Claude Lefèvre
Affiliation:
Institut de Statistique et de Recherche Opérationnelle, Université Libre de Bruxelles, B-1050 Bruxelles, Belgique, E-mail: [email protected]

Abstract

This article is concerned with a loading-dependent model of cascading failure proposed recently by Dobson, Carreras, and Newman [6]. The central problem is to determine the distribution of the total number of initial components that will have finally failed. A new approach based on a closed connection with epidemic modeling is developed. This allows us to consider a more general failure model in which the additional loads caused by successive failures are arbitrarily fixed (instead of being constant as in [6]). The key mathematical tool is provided by the partial joint distributions of order statistics for a sample of independent uniform (0,1) random variables.

Type
Research Article
Copyright
© 2006 Cambridge University Press

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.)

References

REFERENCES

Andersson, H. & Britton, T. (2000). Stochastic epidemic models and their statistical analysis. Lecture Notes in Statistics 151. New York: Springer-Velag.CrossRef
Ball, F.G. & O'Neill, P.D. (1999). The distribution of general final state random variables for stochastic epidemic models. Journal of Applied Probability 36: 473491.Google Scholar
Consul, P.C. (1974). A simple urn model dependent on pre-determined strategy. Sankhya 36: 391399.Google Scholar
Daley, D.J. & Gani, J. (1999) Epidemic modelling: An introduction. Cambridge: Cambridge University Press.
Denuit, M., Lefèvre, C., & Picard, P. (2003). Polynomial structures in order statistics distributions. Journal of Statistical Planning and Inference 113: 151178.Google Scholar
Dobson, I., Carreras, B.A., & Newman, D.E. (2005). A loading-dependent model of probabilistic cascading failure. Probability in the Engineering and Informational Sciences 19: 1532.Google Scholar
Lefèvre, C. & Picard, P. (2005). Non-stationarity and randomization in the Reed–Frost epidemic model. Journal of Applied Probability 42: 950963.Google Scholar
Lefèvre, C. & Utev, S. (1996). Comparing sums of exchangeable Bernoulli random variables. Journal of Applied Probability 33: 285310.Google Scholar
Picard, P. & Lefèvre, C. (1990). A unified analysis of the final state and severity distribution in collective Reed–Frost epidemic processes. Advances in Applied Probability 22: 269294.Google Scholar
Shorack, G.R. & Wellner, J.A. (1986). Empirical processes with applications to statistics. New York: Wiley.
Stadje, W. (1993). Distribution of first-exit times for empirical counting and Poisson processes with moving boundaries. Stochastic Models 9: 91103.Google Scholar
Zacks, S. (1991). Distributions of stopping times for Poisson processes with linear boundaries. Stochastic Models 7: 233242.Google Scholar