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    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s10687-018-0330-z

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    Embargo ends: 18/06/19

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A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge

Research output: Contribution to journalJournal article

E-pub ahead of print
<mark>Journal publication date</mark>18/06/2018
<mark>Journal</mark>Extremes
Number of pages9
<mark>State</mark>E-pub ahead of print
Early online date18/06/18
<mark>Original language</mark>English

Abstract

This paper concerns our approach to the EVA2017 challenge, the aim of which was to predict extreme precipitation quantiles across several sites in the Netherlands. Our approach uses a Bayesian hierarchical structure, which combines Gamma and generalised Pareto distributions. We impose a
spatio-temporal structure in the model parameters via an autoregressive prior.
Estimates are obtained using Markov chain Monte Carlo techniques and spatial interpolation. This approach has been successful in the context of the challenge, providing reasonable improvements over the benchmark.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/s10687-018-0330-z