Handbook of Statistics of Extremes

1. Auflage 2026
ISBN: 978-1-032-51980-7
Verlag: Taylor & Francis Ltd

Buch, Englisch, 800 Seiten, Format (B × H): 178 mm x 254 mm

Reihe: Chapman & Hall/CRC Handbooks of Modern Statistical Methods

Statistics of extremes is a prominent field of research concerned with modeling the risk of occurrence of extreme events, that is, low-probability-high-impact events such as a stock market crash, hurricanes, heatwaves, and widespread flooding.

The Handbook of Statistics of Extremes covers statistical models for univariate, multivariate, and spatio-temporal extreme values. Written by leading experts from around the world, it serves as a key reference for statisticians and data scientists, as well as for professionals working in risk modeling—such as geophysical and climate scientists, financial analysts, and health clinicians and neuroscientists—and as a valuable resource for practitioners and graduate students who wish to deepen their understanding of the statistical modeling of extreme events.

Key Features:

· Presents frequentist and Bayesian methods, as well as AI-based techniques for extreme value analysis.

· Details how to model the frequency, magnitude, and spatio-temporal dependence of extreme events, and how to extrapolate into the tails of a distribution beyond observed data.

· Provides code, data, and other additional materials available here: https://extremestats.github.io/Handbook/.

Zielgruppe

Postgraduate, Professional Reference, and Undergraduate Advanced

Autoren/Hrsg.

Reich, Brian J.

de Carvalho, Miguel

Huser, Raphaël

Naveau, Philippe

Huser, Raphael

Inhaltsverzeichnis

Editors Contributors Basic Symbols Part I Opening Remarks 1. Handbook Outline Part II Univariate Extremes 2. Modeling Univariate Extremes—Why and How 3. Learning About Extreme Value Distributions from Data 4. Bayesian Methods for Extreme Value Analysis 5. Jointly Modeling the Bulk and Tails 6. Regression Models for Extreme Events Part III Multivariate Extremes 7. Multivariate Extreme Value Theory 8. Measures of Extremal Dependence 9. Regression Models for Multivariate Extremes 10. Conditional Extremes Modeling 11. Principal Component Analysis for Multivariate Extremes 12. Clustering Methods for Multivariate Extremes 13. Graphical Models for Multivariate Extremes Part IV Spatial and Temporal Extremes 14. Time Series in Extremes 15. Max-Stable Processes for Spatial Extremes 16. Pareto Processes for Threshold Exceedances in Spatial Extremes 17. Subasymptotic Models for Spatial Extremes 18. Space-Time Modeling of Extremes Part V Emerging Topics 19. Causality and Extremes 20. On the Simulation of Extreme Events with Neural Networks 21. Extreme Quantile Regression with Deep Learning 22. Risk Measures Beyond Quantiles Part VI Applications and Case Studies 23. Detection and Attribution of Extreme Weather Events: A Statistical Review 24. Evaluation of Extreme Forecasts and Projections 25. Statistical Modeling of Extreme Precipitation 26. Statistics of Extremes for Wildfires 27. Statistics of Extremes for Landslides and Earthquakes 28. Tail Risk Analysis for Financial Time Series 29. Statistics of Extremes for the Insurance Industry 30. Statistics of Extremes for Neuroscience 31. Statistics of Extremes for Incomplete Data, with Application to Lifetime and Liability Claim Modeling Sources Index

Über Autor(innen)

Miguel de Carvalho is Professor and Chair of Statistical Data Science at the University of Edinburgh (UoE) as well as Honorary Professor at Universidade de Aveiro. He is elected fellow of the Generative AI Lab (UoE), co-director of the Edinburgh Centre for Financial Innovations, member of the Council of the International Statistical Institute, and past member of the board of directors of the International Society for Bayesian Analysis. Miguel’s research interests include, inter alia, extreme value theory, Bayesian analysis, and the interfaces between statistics and AI. He has been an AE for a variety of top tier journals such as Bayesian Analysis, The American Statistician, The Annals of Applied Statistics, and the Journal of the American Statistical Association. Miguel co-chaired the international conference EVA 2021 in Edinburgh, co-edited the Extremes special issue Bridging Heavy Tails &
AI, and co-founded GLE2N (Glasgow–Edinburgh Extremes Network).

Raphaël Huser is an Associate Professor of Statistics at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, where he leads the Extreme Statistics (XSTAT) research group. His research interests focus on statistics of extremes, risk modeling, spatio-temporal statistics, simulation-based inference, and statistical deep learning, with main applications to climate and geo-environmental data science, finance, and neuroscience. Raphael got several awards for his research, including the 2019 Early Investigator Award from the Section on Statistics and the Environment (ENVR) of the American Statistical Association, and the 2022 Abdel El-Shaarawi Early Investigator Award from The International Environmetrics Society. He has also served as an Associate Editor for several journals, including Extremes, Environmetrics, Spatial Statistics and the Journal of the Royal Statistical Society: Series C.

Philippe Naveau is a CNRS senior researcher at the Laboratoire des Sciences du Climat et de l’Environnement in France. His research interests are extreme value theory, time series analysis, spatial statistics with main applications to statistical climatology and hydrology. He has been part of various national and international grants dealing with climate extremes analysis and statistical risk modeling. Currently, he is the Associate Editor of Annals of Applied Statistics, Extremes and Environmetrics. He has co-organized more than twenty workshops and summer schools on extreme events analysis and he has had the pleasure to
co-advise 20 PhD students.

Brian J. Reich is the Gertrude M. Cox Distinguished Professor of Statistics at North Carolina State University. He is a fellow of the American Statistical Association and member of the International Statistical Institute. He has served as Associate Editor for the Journal of the American Statistical Association, the Annals of Applied Statistics and Biostatistics and as Editor-In-Chief for the Journal of Agricultural, Biological, and Environmental Statistics. His research interests include Bayesian methods, spatial statistics, extreme value analysis and machine learning. A major focus of his research is to develop new models for spatial extreme value analysis and computational approaches to fit these models. In addition to these methodological interests, Brian applies these methods to areas such as meteorology, climate change, air pollution and health effects. He co-authored the textbook Bayesian
Statistical Methods (Chapman & Hall/CRC Press, 2019).