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Reparameterization of extreme value framework for improved Bayesian workflow

Author

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  • Moins, Théo
  • Arbel, Julyan
  • Girard, Stéphane
  • Dutfoy, Anne

Abstract

Using Bayesian methods for extreme value analysis offers an alternative to frequentist ones, with several advantages such as easily dealing with parametric uncertainty or studying irregular models. However, computations can be challenging and the efficiency of algorithms can be altered by poor parametrization choices. The focus is on the Poisson process characterization of univariate extremes and outline two key benefits of an orthogonal parameterization. First, Markov chain Monte Carlo convergence is improved when applied on orthogonal parameters. This analysis relies on convergence diagnostics computed on several simulations. Second, orthogonalization also helps deriving Jeffreys and penalized complexity priors, and establishing posterior propriety thereof. The proposed framework is applied to return level estimation of Garonne flow data (France).

Suggested Citation

  • Moins, Théo & Arbel, Julyan & Girard, Stéphane & Dutfoy, Anne, 2023. "Reparameterization of extreme value framework for improved Bayesian workflow," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:csdana:v:187:y:2023:i:c:s0167947323001184
    DOI: 10.1016/j.csda.2023.107807
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    References listed on IDEAS

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    1. V. Chavez‐Demoulin & A. C. Davison, 2005. "Generalized additive modelling of sample extremes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 207-222, January.
    2. repec:dau:papers:123456789/1908 is not listed on IDEAS
    3. Tiemen Woutersen, 2011. "Consistent Estimation and Orthogonality," Advances in Econometrics, in: Missing Data Methods: Cross-sectional Methods and Applications, pages 155-178, Emerald Group Publishing Limited.
    4. G. O. Roberts & S. K. Sahu, 1997. "Updating Schemes, Correlation Structure, Blocking and Parameterization for the Gibbs Sampler," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(2), pages 291-317.
    5. Albert, Clément & Dutfoy, Anne & Gardes, Laurent & Girard, Stéphane, 2020. "An extreme quantile estimator for the log-generalized Weibull-tail model," Econometrics and Statistics, Elsevier, vol. 13(C), pages 137-174.
    6. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504, October.
    7. Gilleland, Eric & Katz, Richard W., 2016. "extRemes 2.0: An Extreme Value Analysis Package in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i08).
    8. William J. Browne & Fiona Steele & Mousa Golalizadeh & Martin J. Green, 2009. "The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 579-598, June.
    Full references (including those not matched with items on IDEAS)

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