Skip to main content
Log in

Modeling clusters of extreme values

  • Published:
Extremes Aims and scope Submit manuscript

An Erratum to this article was published on 07 January 2016

Abstract

In practice it is important to evaluate the impact of clusters of extreme observations caused by the dependence in time series. The clusters contain consecutive exceedances of time series over a threshold separated by return intervals with consecutive non-exceedances. We derive asymptotically equal distributions of the number of inter-arrival times between events of interest arising both between two consecutive exceedances of a stationary process \(\{R_n:n\ge 1\}\) and between two consecutive non-exceedances. It is found that the distributions are geometric like and corrupted by the extremal index. It is derived that the limit distribution tail of the duration of clusters that is defined as a sum of the random number of the weakly dependent regularly varying inter-arrival times with tail index \(0<\alpha <2\) is bounded by the tail of stable distribution. The inferences are valid when the threshold is taken as a sufficiently high quantile of the underlying process \(\{R_{n}\}\).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ancona-Navarrete, M.A., Tawn, J.A.: A comparison of methods for estimating the extremal index. Extremes 3(1), 5–38 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  • Bartkiewicz, K., Jakubowski, A., Mikosch, T., Wintenberger, O.: Stable limits for sums of dependent infinite variance random variables. Probab. Theory Relat. Fields (2010). doi:10.1007/s00440-010-0276-9

    Google Scholar 

  • Basrak, B., Segers, J.: Regularly varying multivariate time series. Stoch. Process. Appl. 119, 1055–1080 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  • Basrak, B., Krizmanić, D., Segers, J.: A functional limit theorem for partial sums of dependent random variables with infinite variance. http://arxiv.org/abs/1001.1345. Accessed 8 Jan 2010 (2010)

  • Billingsley, P.: Convergence of Probability Measures, 2nd ed. Wiley, New York (1999)

    Book  MATH  Google Scholar 

  • Beirlant, J., Goegebeur, Y., Teugels, J., Segers, J.: Statistics of Extremes: Theory and Applications, vol. 504. Wiley, Chichester, West Sussex (2004)

  • Bradley, R.C.: A central limit theorem for stationary ρ- mixing sequences with infinite variance. Ann. Probab. 16, 313–332 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  • Chernick, M.R., Hsing, T., McCormick, W.P.: Calculating the extremal index for a class of stationary sequences. Adv. Appl. Prob. 23, 835–850 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  • D’Auria, B., Resnick, S.I.: The influence of dependence on data network models of burstiness. Adv. Appl. Prob. 40(1), 60–94 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  • Embrechts, P., Klűppelberg, C., Mikosch, T.: Modeling Extremal Events for Finance and Insurance. Springer, Berlin (1997)

    Book  Google Scholar 

  • Eichner, J.F., Kantelhardt, J.W., Bunde, A., Havlin, S.: Statistics of return intervals in long-term correlated records. Phys. Rev. E 75, 011128 (2007)

    Article  Google Scholar 

  • Ferro, C.A.T., Segers, J.: Inference for clusters of extreme values. J. R. Stat. Soc. B 65, 545–556 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  • Hsing, T., Huesler, J., Leadbetter, M.R.: On the exceedance point process for a stationary sequence. Prob. Theory Relat. Fields 78, 97–112 (1988)

    Article  MATH  Google Scholar 

  • Jessen, A.H., Mikosch, T.: Regularly varying functions. Publications de lInstitut Mathématique Nouvelle série, tome 80(94), 171192 (2006)

    MathSciNet  Google Scholar 

  • Krizmanić, D.: Functional limit theorems for weakly dependent regularly varying time series. PhD thesis, Univ. Zagreb. Available at http://www.math.uniri.hr/dkrizmanic/DKthesis.pdf (2010)

  • Leadbetter, M.R., Lingren, G., Rootzén, H.: Extremes and Related Properties of Random Sequence and Processes, ch. 3. Springer, New York (1983)

    Book  Google Scholar 

  • Leadbetter, M.R., Nandagopalan, L.: On exceedance point processes for stationary sequences under mild oscillation restrictions. Lect. Notes Statist. 51, 69–80 (1989)

    Article  MathSciNet  Google Scholar 

  • Markovich, N.M., Undheim, A., Emstad, P.J.: Classification of slice-based VBR video traffic. Comput. Netw. 53, 1137–1153 (2009)

    Article  MATH  Google Scholar 

  • Markovich, N.M., Kilpi, J.: Bivariate statistical analysis of TCP-flow sizes and durations. Ann. Oper. Res. 170(1), 199–216 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  • Markovich, N.M.: Modeling of dependence in a peer-to-peer video application. In: Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC 2010), pp. 316–320. Caen, France (2010)

    Google Scholar 

  • Markovich, N.M., Krieger, U.R.: Statistical analysis and modeling of peer-to-peer multimedia traffic. In: Kouvatsos, D. (ed.) Next Generation Internet: Performance Evaluation and Applications, LNCS 5233, pp. 37–69. Springer, Heidelberg (2011)

    Google Scholar 

  • Merlevède, F., Peligrad, M., Utev, S.: Recent advances in invariance principles for stationary sequences. Probab. Surv. 3, 1–36 (2006). doi:10.1214/154957806100000202

    Article  MATH  MathSciNet  Google Scholar 

  • Nolan, J.P.: Stable distributions: models for heavy tailed data. In: Progress, ch. 1, Birkhauser, Boston. http://academic2.american.edu/jpnolan/stable/chap1.pdf (2011)

  • O’Brien, G.L.: Extreme values for stationary and Markov sequences. Ann. Probab. 15(1), 281–291 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  • Resnick, S.I.: Heavy-Tail Phenomena. Probabilistic and Statistical Modeling. Springer, New York (2006)

    Google Scholar 

  • Robert, C.Y., Segers, J.: Tails of random sums of a heavy-tailed number of light-tailed terms. Insur. Math. Econ. 43, 8592 (2008)

    Article  MathSciNet  Google Scholar 

  • Robinson, M.E., Tawn, J.A.: Extremal analysis of processes sampled at different frequences. J. R. Stat. Soc. Ser. B 62(1), 117135 (2000). doi:10.1111/1467-9868.00223

    Article  MathSciNet  Google Scholar 

  • Santhanam, M.S., Kantz, H.: Return interval distribution of extreme events and long term memory. Phys. Rev. E 78, 05113 (2008)

    Article  MathSciNet  Google Scholar 

  • Tyran-Kamińska, M.: Convergence to Levy stable processes under some weak dependence conditions. Stoch. Process. Appl. 120, 1629–1650 (2010)

    Article  MATH  Google Scholar 

  • Whitt, W.: Stochastic-Process Limits. An Introduction to Stochastic-Process Limits and their Application to Queues. Springer, New York (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Natalia M. Markovich.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Markovich, N.M. Modeling clusters of extreme values. Extremes 17, 97–125 (2014). https://doi.org/10.1007/s10687-013-0176-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10687-013-0176-3

Keywords

AMS 2000 Subject Classification

Navigation