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An Introduction to Statistical Modeling of Extreme Values
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“The Distribution-Informed Graph Neural Network (DI-GNN) represents a significant advancement in the modeling of rare and extreme events, transcending the limitations of traditional feature engineering by embedding Extreme Value Theory (EVT) principles directly into the learning process.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [29]
In the citing paper
“Unlike heuristic methods, DI-GNN's EVT-based approach provides a principled and robust framework for forecasting extreme climate events [29, 28].”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [29]
In the citing paper
“EVT-based approaches allow for more robust identification of extreme events by modeling the tails of temperature distributions, addressing the inadequacies of fixed percentile thresholds.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [29]
In the citing paper
“DI-GNN incorporates Generalized Pareto Distribution (GPD)-derived descriptors into the feature space, adjacency matrix, and loss function to enhance its sensitivity to rare heatwave occurrences.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [29]
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Self-citation
Metadata
- Authors
- Coles, S.
- Affiliations
- University of Bristol (GB)
- Corresponding author
- Coles, Stuart
- Venue
- Springer Series in Statistics
- Publisher
- Springer London
- Published
- 2001
- ISSN
- 0172-7397 (ISSN-L)
- 2197-568X
- OpenAlex
- W1500657154
- Cited by
- 7,352
- References
- 1
- Citations by year
- 2026: 459 · 2025: 466 · 2024: 393 · 2023: 457 · 2022: 397 · 2021: 483 · 2020: 429 · 2019: 417 · 2018: 398 · 2017: 381 · 2016: 389 · 2015: 371 · 2014: 366 · 2013: 324 · 2012: 314
- FWCI
- 27.09
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer series in statistics (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Probability and Risk Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- extreme value theory, extreme value distribution, tail behavior of distributions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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