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Lundberg inequalities for renewal equations

Published online by Cambridge University Press:  01 July 2016

Gordon E. Willmot*
Affiliation:
University of Waterloo
Jun Cai*
Affiliation:
University of Waterloo
X. Sheldon Lin*
Affiliation:
University of Toronto
*
Postal address: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1. Email address: gewillmo@uwaterloo.ca
∗∗ Current address: Centre for Actuarial Studies, Faculty of Economics and Commerce, University of Melbourne, Victoria 3010, Australia.
∗∗∗ Postal address: Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 3G3.

Abstract

Sharp upper and lower bounds are derived for the solution of renewal equations. These include as special cases exponential inequalities, some of which have been derived for specific renewal equations. Together with the well-known Cramér-Lundberg asymptotic estimate, these bounds give additional information about the behaviour of the solution. Nonexponential bounds, which are of use in connection with defective renewal equations, are also obtained. The results are then applied in examples involving the severity of insurance ruin, age-dependent branching processes, and a generalized type II Geiger counter.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2001 

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References

Bartholomew, D. (1963). An approximate solution of the integral equation of renewal theory. J. R. Statist. Soc. B 25, 432441.Google Scholar
Bhat, U. (1984). Elements of Applied Stochastic Processes, 2nd edn. John Wiley, New York.Google Scholar
Cai, J. and Garrido, J. (1999). A unified approach to the study of tail probabilities of compound distributions. J. Appl. Prob. 36, 10581073.Google Scholar
Cai, J. and Wu, Y. (1997). Some improvements on the Lundberg bound for the ruin probability. Statist. Prob. Lett. 33, 395403.Google Scholar
Deligönül, Z., (1985). An approximate solution of the integral equation of renewal theory. J. Appl. Prob. 22, 926931.CrossRefGoogle Scholar
Fagiuoli, E. and Pellerey, F. (1994). Preservation of certain classes of life distributions under Poisson shock models. J. Appl. Prob. 31, 458465.Google Scholar
Feller, W. (1971). An Introduction to Probability Theory and Its Applications, Vol. 2, 2nd edn. John Wiley, New York.Google Scholar
Gerber, H. (1979). An Introduction to Mathematical Risk Theory. Huebner Foundation, S. S., University of Pennsylvania, Philadelphia.Google Scholar
Gerber, H., Goovaerts, M. and Kaas, R. (1987). On the probability and severity of ruin. ASTIN Bull. 17, 151163.Google Scholar
Glasserman, P. (1997). Bounds and asymptotics for planning critical safety stocks. Operat. Res. 45, 244257.Google Scholar
Grimmett, G. and Stirzaker, D. (1992). Probability and Random Processes, 2nd edn. Oxford University Press.Google Scholar
Kotlyar, V. and Khomenko, L. (1992). Classes of ‘aging’ distributions. Cybernet. Systems 28, 403421.CrossRefGoogle Scholar
Lin, X. (1996). Tail of compound distributions and excess time. J. Appl. Prob. 33, 184195.Google Scholar
Resnick, S. (1992). Adventures in Stochastic Processes. Birkhäuser, Boston.Google Scholar
Ross, S. (1974). Bounds on the delay distribution in GI/G/1 queues. J. Appl. Prob. 11, 417421.CrossRefGoogle Scholar
Ross, S. (1996). Stochastic Processes, 2nd edn. John Wiley, New York.Google Scholar
Shaked, M. and Shanthikumar, J. (1994). Stochastic Orders and Their Applications. Academic Press, San Diego, CA.Google Scholar
Stoyan, D. (1983). Comparison Methods for Queues and Other Stochastic Models. John Wiley, Chichester.Google Scholar
Willmot, G. (1994). Refinements and distributional generalizations of Lundberg's inequality. Insurance Math. Econom. 15, 4963.Google Scholar
Willmot, G. and Lin, X. (2001). Lundberg Approximations for Compound Distributions with Insurance Applications. Springer, New York.Google Scholar