A semiparametric Bayesian approach to extreme value estimation

Fernando Ferraz do Nascimento, Dani Gamerman, Hedibert Freitas Lopes

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

This paper is concerned with extreme value density estimation. The generalized Pareto distribution (GPD) beyond a given threshold is combined with a nonparametric estimation approach below the threshold. This semiparametric setup is shown to generalize a few existing approaches and enables density estimation over the complete sample space. Estimation is performed via the Bayesian paradigm, which helps identify model components. Estimation of all model parameters, including the threshold and higher quantiles, and prediction for future observations is provided. Simulation studies suggest a few useful guidelines to evaluate the relevance of the proposed procedures. They also provide empirical evidence about the improvement of the proposed methodology over existing approaches. Models are then applied to environmental data sets. The paper is concluded with a few directions for future work.

Original languageEnglish (US)
Pages (from-to)661-675
Number of pages15
JournalStatistics and Computing
Volume22
Issue number2
DOIs
StatePublished - Mar 2012
Externally publishedYes

Keywords

  • Bayesian
  • GPD
  • Higher quantiles
  • MCMC
  • Nonparametric estimation of curves
  • Threshold estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

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