You can also find my articles on my Google Scholar profile.
With R. Zimmerman.
In submission, 2025+ (arXiv)
In this paper, we construct a class of copulas that exhibit rotational dependence, is tractable and easy to estimate via univariate methods, and is shown to offer insight through an application to neurological data. Based on the previous technical note “A new family of smooth copulas with arbitrarily irregular densities” (2022).
With S. Engelke, M. Hentschel and F. Röttger.
To appear in Handbook on Statistics of Extremes, 2025+ (arXiv)
This chapter will appear in the upcoming Handbook on Statistics of Extremes. It reviews various recent developments concerning undirected and directed graphical models for extreme values.
With S. Engelke and S. Volgushev.
To appear in Annals of Statistics, 2025+ (arXiv)
This paper proposes a methodology that provably learns extremal graphical models in settings where the dimension is allowed to grow exponentially in the effective sample size. Along the way, we prove a sub-exponential concentration inequality for the empirical version of the extremal variogram, an object of intrest in multivariate and high-dimensional extreme value theory.
It is shown that no multivariate Pareto model, other than the Hüsler–Reiss family, can have the structure of a pairwise interaction model.
With S. Engelke and S. Volgushev.
Annals of Statistics 49(5), 2552-2576 (arXiv, published, supplement)
This paper provides theoretical tools and a new methodology to fit flexible bivariate and spatial tail dependence models that include both asymptotic dependence and independence.