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Abstract

Non-stationary processes have characteristics that change systematically thorough time. In the context of environmental processes, non-stationarity is often apparent because of seasonal effects, perhaps due to different climate patterns in different months, or in the form of trends, possibly due to long-term climate changes. Like the presence of temporal dependence, such departures from the simple assumptions that were made in the derivation of the extreme value characterizations in Chapters 3 and 4 challenge the utility of the standard models. In Chapter 5 we were able to demonstrate that, in a certain sense and subject to specified limitations, the usual extreme value limit models are still applicable in the presence of temporal dependence. No such general theory can be established for non-stationary processes. Results are available for some very specialized forms of non-stationarity, but these are generally too restrictive to be of use for describing the patterns of non-stationarity found in real processes. Instead, it is usual to adopt a pragmatic approach of using the standard extreme value models as basic templates that can be enhanced by statistical modeling.

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Further Reading

  • Coles, S. G., Tawn, J. A., and Smith, R. L. (1994). A seasonal Markov model for extremely low temperatures. Environmetrics 5, 221–239.

    Article  Google Scholar 

  • Leadbetter, M. R. (1983). Extremes and local dependence in stationary-sequences. Zeit. Wahrscheinl.-theorie 65, 291–306.

    Article  MathSciNet  MATH  Google Scholar 

  • Rootzén, H. (1986). Extreme value theory for moving average processes. Annals of Probability 14, 612–652.

    Article  MathSciNet  MATH  Google Scholar 

  • Smith, R. L. (1989b). A survey of nonregular problems. In Proceedings of the 47th meeting of the I.S.I.,pages 353–372. International Statistical Institute.

    Google Scholar 

  • Smith, R. L. and NAYLOR, J. C. (1987). A comparison of maximum likelihood and Bayesian estimators for the three-parameter Weibull distribution. Applied Statistics 36, 358–369.

    Article  MathSciNet  Google Scholar 

  • Smith, R: L., Tawn, J. A., and Yuen, H. K. (1990). Statistics of multivariate extremes. International Statistical Review 58, 47–58.

    Google Scholar 

  • Smith, R. L. (1999). Bayesian and frequentist approaches to parametric predictive inference (with discussion). In Bernardo, J. M., Berger, J. O., Dawid, A. P., and Smith, A. F. M., editors, Bayesian Statistics 6,pages 589–612. Oxford University Press.

    Google Scholar 

  • Scarf, P. A. and Laycock, P. J. (1996). Estimation of extremes in corrosion engineering. Journal of Applied Statistics 23, 621–643.

    Article  Google Scholar 

  • Moore, R. J. (1987). Combined regional flood frequency analysis and regression on catchment characteristics by maximum likelihood estimation. In Singh, V. P., editor, Regional Flood Frequency Analysis, pages 119–131. Reidel, Dordrecht.

    Chapter  Google Scholar 

  • Smith, R. L. (1994). Multivariate threshold methods. In Galambos, J., Lechner, J., and Simiu, E., editors, Extreme Value Theory and Applications, pages 225–248. Kluwer, Dordrecht.

    Chapter  Google Scholar 

  • Davison, A. C. and Smith, R. L. (1990). Models for exceedances over high thresholds (with discussion). Journal of the Royal Statistical Society, B 52, 393–442.

    MathSciNet  MATH  Google Scholar 

  • Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. Chapman and Hall, London.

    Google Scholar 

  • Hall, P. and Tajvidi, N. (2000a). Distribution and dependence function estimation for bivariate extreme value distributions. Bernoulli 6, 835844.

    Google Scholar 

  • Rosen, G. and Cohen, A. (1994). Extreme percentile regression. In Statistical Theory and Computational Aspects of Smoothing: Proceedings of the COMPSTAT ‘84 satellite meeting, pages 200–214. Physica-Verlag, Heidelberg.

    Google Scholar 

  • Chavez-Demoulin, V. (1999). Two problems in environmental statistics: capture-recapture models and smooth extremal models. PhD thesis, EPFL, Lausanne, Switzerland.

    Google Scholar 

  • Pauli, F. and Coles, S. G. (2001). Penalized likelihood inference in extreme value analyses. Journal of Applied Statistics 28, 547–560.

    Google Scholar 

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© 2001 Springer-Verlag London

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Coles, S. (2001). Extremes of Non-stationary Sequences. In: An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics. Springer, London. https://doi.org/10.1007/978-1-4471-3675-0_6

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  • DOI: https://doi.org/10.1007/978-1-4471-3675-0_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-874-4

  • Online ISBN: 978-1-4471-3675-0

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