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Multivariate max-stable spatial processes

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  • Marc G. Genton
  • Simone A. Padoan
  • Huiyan Sang

Abstract

Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.

Suggested Citation

  • Marc G. Genton & Simone A. Padoan & Huiyan Sang, 2015. "Multivariate max-stable spatial processes," Biometrika, Biometrika Trust, vol. 102(1), pages 215-230.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:1:p:215-230.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu066
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    Cited by:

    1. Moreno Bevilacqua & Alfredo Alegria & Daira Velandia & Emilio Porcu, 2016. "Composite Likelihood Inference for Multivariate Gaussian Random Fields," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 448-469, September.
    2. Hashorva, Enkelejd, 2018. "Representations of max-stable processes via exponential tilting," Stochastic Processes and their Applications, Elsevier, vol. 128(9), pages 2952-2978.
    3. Kim, Mihyun & Kokoszka, Piotr, 2022. "Extremal dependence measure for functional data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    4. Hashorva, Enkelejd & Kume, Alfred, 2021. "Multivariate max-stable processes and homogeneous functionals," Statistics & Probability Letters, Elsevier, vol. 173(C).

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