%0 Journal Article %1 Barlow2018 %A Barlow, Anna Maria %A Rohrbeck, Christian %A Sharkey, Paul %A Shooter, Rob %A Simpson, Emma S. %D 2018 %J Extremes %K extremes, precipitation, statistics %N 3 %P 431--439 %R 10.1007/s10687-018-0330-z %T A Bayesian spatio-temporal model for precipitation extremes---STOR team contribution to the EVA2017 challenge %U https://doi.org/10.1007/s10687-018-0330-z %V 21 %X 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.

@article{Barlow2018, 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.}, added-at = {2018-09-25T12:00:09.000+0200}, author = {Barlow, Anna Maria and Rohrbeck, Christian and Sharkey, Paul and Shooter, Rob and Simpson, Emma S.}, biburl = {https://www.bibsonomy.org/bibtex/2512d54ff2b3747bf5a408739c99a2f3f/rutgerdankers}, day = 01, description = {A Bayesian spatio-temporal model for precipitation extremes—STOR team contribution to the EVA2017 challenge | SpringerLink}, doi = {10.1007/s10687-018-0330-z}, interhash = {f390188c49bb1aed60bb11254cf9b76e}, intrahash = {512d54ff2b3747bf5a408739c99a2f3f}, issn = {1572-915X}, journal = {Extremes}, keywords = {extremes, precipitation, statistics}, month = sep, number = 3, pages = {431--439}, timestamp = {2018-09-25T12:00:09.000+0200}, title = {A Bayesian spatio-temporal model for precipitation extremes---STOR team contribution to the EVA2017 challenge}, url = {https://doi.org/10.1007/s10687-018-0330-z}, volume = 21, year = 2018 }