Reparameterization of extreme value framework for improved Bayesian workflow

Théo Moins, Julyan Arbel, Stéphane Girard and Anne Dutfoy

Computational Statistics & Data Analysis, 2023, vol. 187, issue C

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).

Keywords: Bayesian inference; Extreme value theory; Reparameterization; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:187:y:2023:i:c:s0167947323001184

DOI: 10.1016/j.csda.2023.107807

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