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Efficient inference and simulation for elliptical Pareto processes

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  • Emeric Thibaud
  • Thomas Opitz

Abstract

Recent advances in extreme value theory have established $\ell $-Pareto processes as the natural limits for extreme events defined in terms of exceedances of a risk functional. In this paper we provide methods for the practical modelling of data based on a tractable yet flexible dependence model. We introduce the class of elliptical $\ell $-Pareto processes, which arise as the limits of threshold exceedances of certain elliptical processes characterized by a correlation function and a shape parameter. An efficient inference method based on maximizing a full likelihood with partial censoring is developed. Novel procedures for exact conditional and unconditional simulation are proposed. These ideas are illustrated using precipitation extremes in Switzerland.

Suggested Citation

  • Emeric Thibaud & Thomas Opitz, 2015. "Efficient inference and simulation for elliptical Pareto processes," Biometrika, Biometrika Trust, vol. 102(4), pages 855-870.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:4:p:855-870.
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    File URL: http://hdl.handle.net/10.1093/biomet/asv045
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    Citations

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    Cited by:

    1. R de Fondeville & A C Davison, 2018. "High-dimensional peaks-over-threshold inference," Biometrika, Biometrika Trust, vol. 105(3), pages 575-592.
    2. Federica Stolf & Antonio Canale, 2023. "A hierarchical Bayesian non‐asymptotic extreme value model for spatial data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
    3. Raphaël Huser & Thomas Opitz & Emeric Thibaud, 2021. "Max‐infinitely divisible models and inference for spatial extremes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 321-348, March.
    4. Robert, Christian Y., 2022. "Testing for changes in the tail behavior of Brown–Resnick Pareto processes," Stochastic Processes and their Applications, Elsevier, vol. 144(C), pages 312-368.
    5. Samuel A. Morris & Brian J. Reich & Emeric Thibaud, 2019. "Exploration and Inference in Spatial Extremes Using Empirical Basis Functions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 555-572, December.
    6. Raphaël de Fondeville & Anthony C. Davison, 2022. "Functional peaks‐over‐threshold analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1392-1422, September.
    7. Samuel A. Morris & Brian J. Reich & Emeric Thibaud & Daniel Cooley, 2017. "A space-time skew-t model for threshold exceedances," Biometrics, The International Biometric Society, vol. 73(3), pages 749-758, September.
    8. Daniela Castro-Camilo & Raphaël Huser & Håvard Rue, 2019. "A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 517-534, September.
    9. Raphaël Huser & Marc G. Genton, 2016. "Non-Stationary Dependence Structures for Spatial Extremes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 470-491, September.
    10. Lee, Xing Ju & Hainy, Markus & McKeone, James P. & Drovandi, Christopher C. & Pettitt, Anthony N., 2018. "ABC model selection for spatial extremes models applied to South Australian maximum temperature data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 128-144.
    11. Jennifer L. Wadsworth, 2015. "On the occurrence times of componentwise maxima and bias in likelihood inference for multivariate max-stable distributions," Biometrika, Biometrika Trust, vol. 102(3), pages 705-711.

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