INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles

Thomas Opitz, Raphaël Huser*, Haakon Bakka, Håvard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

This work is motivated by the challenge organized for the 10th International Conference on Extreme-Value Analysis (EVA2017) to predict daily precipitation quantiles at the 99.8 % level for each month at observed and unobserved locations. Our approach is based on a Bayesian generalized additive modeling framework that is designed to estimate complex trends in marginal extremes over space and time. First, we estimate a high non-stationary threshold using a gamma distribution for precipitation intensities that incorporates spatial and temporal random effects. Then, we use the Bernoulli and generalized Pareto (GP) distributions to model the rate and size of threshold exceedances, respectively, which we also assume to vary in space and time. The latent random effects are modeled additively using Gaussian process priors, which provide high flexibility and interpretability. We develop a penalized complexity (PC) prior specification for the tail index that shrinks the GP model towards the exponential distribution, thus preventing unrealistically heavy tails. Fast and accurate estimation of the posterior distributions is performed thanks to the integrated nested Laplace approximation (INLA). We illustrate this methodology by modeling the daily precipitation data provided by the EVA2017 challenge, which consist of observations from 40 stations in the Netherlands recorded during the period 1972–2016. Capitalizing on INLA’s fast computational capacity and powerful distributed computing resources, we conduct an extensive cross-validation study to select the model parameters that govern the smoothness of trends. Our results clearly outperform simple benchmarks and are comparable to the best-scoring approaches of the other teams.

Original languageEnglish (US)
Pages (from-to)441-462
Number of pages22
JournalExtremes
Volume21
Issue number3
DOIs
StatePublished - Sep 1 2018

Keywords

  • 62E20
  • 62M30
  • 62P12
  • Bayesian hierarchical modeling
  • Extreme-Value Analysis Conference challenge
  • Extreme-Value Theory
  • Generalized Pareto distribution
  • High quantile estimation
  • Integrated nested Laplace approximation (INLA)

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)

Fingerprint

Dive into the research topics of 'INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles'. Together they form a unique fingerprint.

Cite this