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On the exceedance point process for a stationary sequence
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  • Published: March 1988

On the exceedance point process for a stationary sequence

  • T. Hsing1,
  • J. Hüsler2 &
  • M. R. Leadbetter3 

Probability Theory and Related Fields volume 78, pages 97–112 (1988)Cite this article

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  • 168 Citations

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Summary

It is known that the exceedance points of a high level by a stationary sequence are asymptotically Poisson as the level increases, under appropriate long range and local dependence conditions. When the local dependence conditions are relaxed, clustering of exceedances may occur, based on Poisson positions for the clusters. In this paper a detailed analysis of the exceedance point process is given, and shows that, under wide conditions, any limiting point process for exceedances is necessarily compound Poisson. More generally the possible random measure limits for normalized exceedance point processes are characterized. Sufficient conditions are also given for the existence of a point process limit. The limiting distributions of extreme order statistics are derived as corollaries.

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Author information

Authors and Affiliations

  1. Department of Statistics, Texas A&M University, 77843, College Station, TX

    T. Hsing

  2. Department of Mathematics, Universität Bern, Bern, Switzerland

    J. Hüsler

  3. Department of Statistics, University of North Carolina, 27154, Chapel Hill, NC

    M. R. Leadbetter

Authors
  1. T. Hsing
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  2. J. Hüsler
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  3. M. R. Leadbetter
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Additional information

This research has been supported by the Air Force Office of Scientific Research Grant No. F 49620 85 C 0144 and the Katholieke Universiteit Leuven

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Cite this article

Hsing, T., Hüsler, J. & Leadbetter, M.R. On the exceedance point process for a stationary sequence. Probab. Th. Rel. Fields 78, 97–112 (1988). https://doi.org/10.1007/BF00718038

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  • Received: 20 May 1985

  • Revised: 12 December 1986

  • Issue Date: March 1988

  • DOI: https://doi.org/10.1007/BF00718038

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Keywords

  • Detailed Analysis
  • Stochastic Process
  • Probability Theory
  • Long Range
  • Order Statistic
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