The MELBS team winning entry for the EVA2017 competition for spatiotemporal prediction of extreme rainfall using generalized extreme value quantiles
DOI10.1007/s10687-018-0321-0zbMath1404.62134OpenAlexW2802256130WikidataQ111094295 ScholiaQ111094295MaRDI QIDQ1792637
Kate Saunders, Alec G. Stephenson, Laleh Tafakori
Publication date: 12 October 2018
Published in: Extremes (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10687-018-0321-0
Inference from spatial processes (62M30) Asymptotic distribution theory in statistics (62E20) Applications of statistics to environmental and related topics (62P12) Statistics of extreme values; tail inference (62G32)
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- A note on the validity of cross-validation for evaluating autoregressive time series prediction
- Statistical inference using extreme order statistics
- On the maximum likelihood estimator for the generalized extreme-value distribution
- Editorial: Special issue on the extreme value analysis conference challenge ``prediction of extremal precipitation
- A hierarchical model for the analysis of spatial rainfall extremes
- Maximum likelihood estimation in a class of nonregular cases
- Improvements on Cross-Validation: The .632+ Bootstrap Method
- Inference for Clusters of Extreme Values
- Space–Time Modelling of Extreme Events
- An introduction to statistical modeling of extreme values
- The elements of statistical learning. Data mining, inference, and prediction
- Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change
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