Supporting Information

Handbook of Statistics of Extremes

Supplementary Materials

This page is currently under construction.

Codes

Part I. Opening Remarks

• 1. Handbook Outline
  

Part II. Univariate Extremes

• 2. Modeling Univariate Extremes—Why and How
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• 3. Learning About Extreme Value Distributions from Data
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• 4. Bayesian Methods for Extreme Value Analysis
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• 5. Jointly Modeling the Bulk and Tails
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• 6. Regression Models for Extreme Events
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Part III. Multivariate Extremes

• 7. Multivariate Extreme Value Theory
  Code: None.
• 8. Measures of Extremal Dependence
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• 9. Regression Models for Multivariate Extremes
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• 10. Conditional Extremes Modeling
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• 11. Principal Component Analysis for Multivariate Extremes
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• 12. Clustering Methods for Multivariate Extremes
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• 13. Graphical Models for Multivariate Extremes
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Part IV. Spatial and Temporal Extremes

• 14. Time Series in Extremes
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• 15. Max-Stable Processes for Spatial Extremes
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• 16. Pareto Processes for Threshold Exceedances in Spatial Extremes
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• 17. Subasymptotic Models for Spatial Extremes
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• 18. Space-Time Modeling of Extremes
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Part V. Emerging Topics

• 19. Causality and Extremes
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• 20. On the Simulation of Extreme Events with Neural Networks
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• 21. Extreme Quantile Regression with Deep Learning
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• 22. Risk Measures Beyond Quantiles
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Part VI. Applications and Case Studies

• 23. Detection and Attribution of Extreme Weather Events: A Statistical Review
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• 24. Evaluation of Extreme Forecasts and Projections
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• 25. Statistical Modeling of Extreme Precipitation
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• 26. Statistics of Extremes for Wildfires
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• 27. Statistics of Extremes for Landslides and Earthquakes
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• 28. Tail Risk Analysis for Financial Time Series
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• 29. Statistics of Extremes for the Insurance Industry
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• 30. Statistics of Extremes for Neuroscience
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• 31. Statistics of Extremes for Incomplete Data, with Application to Lifetime and Liability Claim Modeling
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