Abstract
AbstractStatistical methods are proposed to select homogeneous regions when analyzing spatial block maxima data, such as in extreme event attribution studies. Here, homogeneitity refers to the fact that marginal model parameters are the same at different locations from the region. The methods are based on classical hypothesis testing using Wald-type test statistics, with critical values obtained from suitable parametric bootstrap procedures and corrected for multiplicity. A large-scale Monte Carlo simulation study finds that the methods are able to accurately identify homogeneous locations, and that pooling the selected locations improves the accuracy of subsequent statistical analyses. The approach is illustrated with a case study on precipitation extremes in Western Europe. The methods are implemented in an R package that allows for easy application in future extreme event attribution studies.
Funder
Bundesministerium für Bildung und Forschung
Ruhr-Universität Bochum
Publisher
Springer Science and Business Media LLC
Subject
Economics, Econometrics and Finance (miscellaneous),Engineering (miscellaneous),Statistics and Probability
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