A regionalisation approach for rainfall based on extremal dependence
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Publication:2028583
DOI10.1007/s10687-020-00395-yzbMath1481.60098arXiv1907.05750OpenAlexW3092236644WikidataQ114182807 ScholiaQ114182807MaRDI QIDQ2028583
Alec G. Stephenson, D. J. Karoly, K. R. Saunders
Publication date: 1 June 2021
Published in: Extremes (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1907.05750
Applications of statistics to environmental and related topics (62P12) Sampling theory, sample surveys (62D05) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)
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- Estimating the Number of Clusters in a Data Set Via the Gap Statistic
- Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion?
- Non-stationary dependence structures for spatial extremes
- Regular conditional distributions of continuous max-infinitely divisible random fields
- Optimal weighted nearest neighbour classifiers
- A spectral representation for max-stable processes
- Models for stationary max-stable random fields
- Time-varying extreme value dependence with application to leading European stock markets
- Statistical post-processing of forecasts for extremes using bivariate Brown-Resnick processes with an application to wind gusts
- Variograms for spatial max-stable random fields
- Extreme Value Modeling and Risk Analysis
- Modelling pairwise dependence of maxima in space
- A Survey of Recent Advances in Hierarchical Clustering Algorithms
- Finding Groups in Data
- Local Likelihood Estimation of Complex Tail Dependence Structures, Applied to U.S. Precipitation Extremes
- Statistics for Spatial Data
- Likelihood-Based Inference for Max-Stable Processes
- An introduction to statistical modeling of extreme values
- Statistical modeling of spatial extremes
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