1399 31 17990 2 67114 document 17990 EVA 2023 Challenge Data.zip application/zip e8e46630d751ddebc1ad01b0a4f59be2 MD5 2565212 2024-05-09 14:15:24 https://researchdata.bath.ac.uk/1399/1/EVA%202023%20Challenge%20Data.zip 1399 1 1 application/zip other These data sets were generated in R for the Data Challenge organised as part of the Extreme Value Analysis 2023 conference. en public cc_by

EVA 2023 Challenge Data.zip

data archive 11820 disk0/00/00/13/99 2025-04-03 10:39:50 2025-04-28 08:56:36 2025-04-03 10:39:50 data_collection show Rohrbeck Christian cr777@bath.ac.uk 0000-0002-4657-6908 University of Bath TRUE Simpson Emma emma.simpson@ucl.ac.uk 0000-0001-9183-5756 University College London FALSE Tawn Jonathan j.tawn@lancaster.ac.uk Lancaster University FALSE GJ0090 dept_math_sci Extreme Value Theory, Extreme Value Analysis 2023 conference This data set provides the datasets generated by the three creators (data challenge organisers) and subsequently provided to the participants of the EVA 2023 Data Challenge. The dataset aims to capture the variety of contexts experienced in the analysis of environmental extremes data. This involves both univariate and multivariate problems. The univariate extremes problems involve inference for extreme quantiles when faced with additional complications such as covariates; data missing at random; and the need to convert the inference into design levels which account for different losses from over- and under-design. The data set consists of five data files: 1. Amaurot: Training data given to the participants for Tasks 1 and 2 2. AmaurotTestSet: Collection of test data points for which predictions had to be submitted 3. Coputopia: Data participants had to consider for Task 3 4. UtopulaU1 + UtopulaU2: Data participants had to consider for Task 4 The aim of this dataset, developed for the Data Challenge, is to assess performance in multivariate extremes in a way that is independent of marginal extremes abilities. Consequently, the multivariate problems relate to data where the univariate marginal distributions are all known. 2025-03-29 University of Bath public HostingInstitution University of Bath University of Bath https://doi.org/10.13039/501100000835 Data is entirely simulated using methodology from the statistical research area of Extreme Value Theory. Further details on the methodology can be found in the associated paper. Data was simulated using the statistical programming language R. en 1 10.15125/BATH-01399 https://doi.org/10.1007/s10687-025-00508-5 pub open Dataset