Conditional independence, graphical models and sparsity are key notions for parsimonious
statistical models and for understanding the structural relationships in the data. The theory of …

Environmental data science for spatial extremes has traditionally relied heavily on max-
stable processes. Even though the popularity of these models has perhaps peaked with …

Abstract Support Vector Machines (SVMs) are one of the most popular supervised learning
models to classify using a hyperplane in an Euclidean space. Similar to SVMs, tropical …

Graphical models in extremes have emerged as a diverse and quickly expanding research
area in extremal dependence modeling. They allow for parsimonious statistical methodology …

We study the joint occurrence of large values of a Markov random field or undirected
graphical model associated to a block graph. On such graphs, containing trees as special …

Recursive max-linear vectors model causal dependence between node variables by a
structural equation model, expressing each node variable as a max-linear function of its …

Max-linear Bayesian networks have emerged as highly applicable models for causal
inference from extreme value data. However, conditional independence (CI) for max-linear …

Principal component analysis (PCA) is one of the most popular dimension reduction
techniques in statistics and is especially powerful when a multivariate distribution is …

The following sections are a start on organizing references on stable distributions by topic. It
is far from complete. Starting on page 23 there is an extensive list of papers, most on stable …

Recursive max-linear vectors provide models for the causal dependence between large
values of observed random variables as they are supported on directed acyclic graphs …