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Bayesian Variable Selection in Generalized Extreme Value Regression: Modeling Annual Maximum Temperature

Author

Listed:
  • Jorge Castillo-Mateo

    (Department of Statistical Methods, University of Zaragoza, 50009 Zaragoza, Spain)

  • Jesús Asín

    (Department of Statistical Methods, University of Zaragoza, 50009 Zaragoza, Spain)

  • Ana C. Cebrián

    (Department of Statistical Methods, University of Zaragoza, 50009 Zaragoza, Spain)

  • Jesús Mateo-Lázaro

    (Department of Earth Sciences, University of Zaragoza, 50009 Zaragoza, Spain)

  • Jesús Abaurrea

    (Department of Statistical Methods, University of Zaragoza, 50009 Zaragoza, Spain)

Abstract

In many applications, interest focuses on assessing relationships between covariates and the extremes of the distribution of a continuous response. For example, in climate studies, a usual approach to assess climate change has been based on the analysis of annual maximum data. Using the generalized extreme value (GEV) distribution, we can model trends in the annual maximum temperature using the high number of available atmospheric covariates. However, there is typically uncertainty in which of the many candidate covariates should be included. Bayesian methods for variable selection are very useful to identify important covariates. However, such methods are currently very limited for moderately high dimensional variable selection in GEV regression. We propose a Bayesian method for variable selection based on a stochastic search variable selection (SSVS) algorithm proposed for posterior computation. The method is applied to the selection of atmospheric covariates in annual maximum temperature series in three Spanish stations.

Suggested Citation

  • Jorge Castillo-Mateo & Jesús Asín & Ana C. Cebrián & Jesús Mateo-Lázaro & Jesús Abaurrea, 2023. "Bayesian Variable Selection in Generalized Extreme Value Regression: Modeling Annual Maximum Temperature," Mathematics, MDPI, vol. 11(3), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:759-:d:1055427
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    References listed on IDEAS

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