# Efficient inference and simulation for elliptical Pareto processes

arXiv: https://arxiv.org/abs/1401.0168 · HTML: https://mlsanity.com/paper/1401.0168/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2013.

## Built on by (most cited first, 2 of 2)

- [Neural Networks for Parameter Estimation in Intractable Models](https://mlsanity.com/paper/2107.14346/) (2021)
- [Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders](https://mlsanity.com/paper/2512.06348/) (2025)

## Abstract (arXiv)

Recent advances in extreme value theory have established ℓ-Pareto processes as the natural limits for extreme events defined in terms of exceedances of a risk functional. Here we provide methods for the practical modelling of data based on a tractable yet flexible dependence model. We introduce the class of elliptical ℓ-Pareto processes, which arise as the limit of threshold exceedances of certain elliptical processes characterized by a correlation function and a shape parameter. An efficient inference method based on maximizing a full likelihood with partial censoring is developed. Novel procedures for exact conditional and unconditional simulation are proposed. These ideas are illustrated using precipitation extremes in Switzerland.