Covers extremal limit theorems, basic statistical analysis, and applications to multivariate extremes, emphasizing the importance of understanding the distribution of maxima.
Explores Bayesian techniques for extreme value problems, including Markov Chain Monte Carlo and Bayesian inference, emphasizing the importance of prior information and the use of graphs.
Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.