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Extreme value estimation for discretely sampled continuous processes

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Abstract

In environmental applications of extreme value statistics, the underlying stochastic process is often modeled either as a max-stable process in continuous time/space or as a process in the domain of attraction of such a max-stable process. In practice, however, the processes are typically only observed at discrete points and one has to resort to interpolation to fill in the gaps. We discuss the influence of such an interpolation on estimators of marginal parameters as well as estimators of the exponent measure. In particular, natural conditions on the fineness of the observational scheme are developed which ensure that asymptotically the interpolated estimators behave in the same way as the estimators which use fully observed continuous processes.

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References

  • Adler, R.J.: An Introduction to Continuity, Extrema, and Related Topics for General Gaussian Processes. IMS Lecture Notes, vol. 12 (1990)

  • Albin, J.M.P.: On extremal theory for stationary processes. Ann. Probab. 18, 92–128 (1990)

    Article  MathSciNet  Google Scholar 

  • Buhl, S., Klüppelberg, C.: Anisotropic Brown-Resnick space-time processes: estimation and model assessment. Extremes 19, 627–660 (2016)

    Article  MathSciNet  Google Scholar 

  • Daley, D.J., Vere-Jones, D.: An Introduction to the Theory of Point Processes, Vol. I: Elementary Theory and Methods. Springer (2003)

  • Daley, D.J., Vere-Jones, D.: An Introduction to the Theory of Point Processes, Vol. II: General Theory and Structure. Springer (2008)

  • de Haan, L., Ferreira, A.: Extreme value theory. Springer (2006)

  • de Haan, L., Lin, T.: Weak consistency of extreme value estimators in C[0, 1]. Ann. Statist. 31, 1996–2012 (2003)

    Article  MathSciNet  Google Scholar 

  • de Haan, L., Pereira, T.T.: Spatial extremes: models for the stationary case. Ann. Statist. 34, 146–168 (2006)

    Article  MathSciNet  Google Scholar 

  • Dombry, C., Éyi-Minko, F., Ribatet, M.: Conditional simulation of max-stable processes. Biometrika 100, 111–124 (2013)

    Article  MathSciNet  Google Scholar 

  • Drees, H.: Extreme Quantile estimation for dependent data with applications to finance. Bernoulli 9, 617–657 (2003)

    Article  MathSciNet  Google Scholar 

  • Einmahl, J.H.J., Lin, T.: Asymptotic normality of extreme value estimators on C[0, 1]. Ann. Statist. 34, 469–492 (2006)

    Article  MathSciNet  Google Scholar 

  • Falk, M., Hofmann, M., Zott, M.: On generalized max-linear models and their statistical interpolation. J. Appl. Probab. 52, 736–751 (2015)

    Article  MathSciNet  Google Scholar 

  • Fuentes, M., Henry, J., Reich, B.: Nonparametric spatial models for extremes: application to extreme temperature data. Extremes 16, 75–101 (2013)

    Article  MathSciNet  Google Scholar 

  • Genton, M.G., Padoan, S.A., Dang, H.: Multivariate max-stable spatial processes. Biometrika 102, 215–230 (2015)

    Article  MathSciNet  Google Scholar 

  • Lehmann, E.A., Phatak, A., Stephenson, A.G., Lau, R.: Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change. Environmetrics 27, 239–251 (2016)

    Article  MathSciNet  Google Scholar 

  • Oesting, M., Schlather, M.: Conditional sampling for max-stable processes with a mixed moving maxima representation. Extremes 17, 157–192 (2014)

    Article  MathSciNet  Google Scholar 

  • Oesting, M., Schlather, M., Friederichs, P.: Statistical post-processing of forecasts for extremes using bivariate brown-resnick processes with an application to wind gusts. Extremes 20, 309–332 (2017)

    Article  MathSciNet  Google Scholar 

  • Piterbarg, V.I.: Discrete and continuous time extremes of Gaussian processes. Extremes 7, 161—177 (2004)

    MathSciNet  MATH  Google Scholar 

  • Turkman, K.F.: Discrete and continuous time extremes of stationary processes. In: Turkman, K.F (ed.) , vol. 30, pp. 565–581. Elsevier (2012)

  • Wang, Y., Stoev, S.A.: Conditional sampling for spectrally discrete max-stable random fields. Adv. Appl. Probab. 43, 461–483 (2011)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

L. de Haan and F. Turkman have been partly funded by FCT - Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013. H. Drees has been partly supported by DFG project DR 271/6-2 within the research unit FOR 1735. We thank two anonymous referees whose constructive remarks led to an improvement of the presentation.

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Correspondence to Holger Drees.

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Drees, H., de Haan, L. & Turkman, F. Extreme value estimation for discretely sampled continuous processes. Extremes 21, 533–550 (2018). https://doi.org/10.1007/s10687-018-0313-0

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  • DOI: https://doi.org/10.1007/s10687-018-0313-0

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