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On the rate of convergence for extremes of mean square differentiable stationary normal processes

Published online by Cambridge University Press:  14 July 2016

Marie F. Kratz*
Affiliation:
Université René Descartes
Holger Rootzén*
Affiliation:
Chalmers University of Technology
*
Postal address: UFR de Mathématiques et Informatique, Université René Descartes, Paris V, 45 rue des Saints-Pères, 75270 Paris Cedex 06, France. e-mail: kratz@math-info.univ-paris5.fr
∗∗Postal address: Department of Mathematics, Chalmers University of Technology, S-41962 Göteborg, Sweden. e-mail: rootzen@math.chalmers.se

Abstract

Let ξ (t); t ≧ 0 be a normalized continuous mean square differentiable stationary normal process with covariance function r(t). Further, let and set . We give bounds which are roughly of order Τ –δ for the rate of convergence of the distribution of the maximum and of the number of upcrossings of a high level by ξ (t) in the interval [0, T]. The results assume that r(t) and r′(t) decay polynomially at infinity and that r (t) is suitably bounded. For the number of upcrossings it is in addition assumed that r(t) is non-negative.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1997 

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References

Barbour, A. D., Holst, L. and Janson, S. (1992) Poisson Approximation. Oxford Science Publishers, Oxford.CrossRefGoogle Scholar
Berman, S. M. (1971) Excursions above high levels for stationary Gaussian processes. Pacific J. Math. 36, 6379.CrossRefGoogle Scholar
Cramér, H. (1965) A limit theorem for the maximum values of certain stochastic processes. Theory Prob. Appl. 10, 126128.CrossRefGoogle Scholar
Cramer, H. and Leadbetter, M. R. (1967) Stationary and Related Stochastic Processes. Wiley, New York.Google Scholar
Falk, M., Hüsler, J. and Reiss, R. D. (1994) Laws of Small Numbers: Extremes and Rare Events. Birkhaüser, Basel.Google Scholar
Holst, L. and Janson, S. (1990) Poisson approximation using the Stein-Chen method and coupling: number of exceedances of Gaussian random variables. Ann. Prob. 18, 713723.CrossRefGoogle Scholar
Leadbetter, M. R., Lindgren, G. and Rootzén, H. (1978) Conditions for the convergence in distribution of maxima of stationary normal processes. Stoch. Proc. Appl. 8, 131139.CrossRefGoogle Scholar
Leadbetter, M. R., Lindgren, G. and Rootzén, H. (1983) Extremes and Related Properties of Random Sequences and Processes. Springer, New York.CrossRefGoogle Scholar
Pickersgill, E. A. and Piterbarg, V. I. (1987) Estimation of the rate of convergence in a Poisson limit theorem for the excursions of stationary Gaussian processes. Theory Prob. Math. Statist. 36 (in Russian).Google Scholar
Pickands, J. III (1969) Upcrossing probabilities for stationary Gaussian processes. Trans. Amer. Math. Soc. 145, 5173.CrossRefGoogle Scholar
Piterbarg, V. I. (1982) Large deviations of random processes close to Gaussian ones. Theory Prob. Appl. 27, 504524.CrossRefGoogle Scholar
Rootzen, H. (1983) The rate of convergence of extremes of stationary normal sequences. Adv. Appl. Prob. 15, 5480.CrossRefGoogle Scholar
Volkonskii, V. A. and Rozanov, Yu. A. (1961) Some limit theorems for random functions II. Theory Prob. Appl. 6, 186198.CrossRefGoogle Scholar