Exact Simulation of Max-Infinitely Divisible Processes

TL;DR

This paper develops exact, efficient simulation algorithms for max-infinitely divisible processes, including new models with flexible dependence structures, improving over approximate methods and aiding statistical inference in extreme-value analysis.

Contribution

The paper introduces generalized simulation algorithms for max-id processes, including adaptive rejection sampling and new models with flexible tail dependence.

Findings

Simulation algorithm is highly accurate and efficient.

Outperforms traditional approximate sampling schemes.

New max-id models with versatile dependence structures are proposed.

Abstract

Max-infinitely divisible (max-id) processes play a central role in extreme-value theory and include the subclass of all max-stable processes. They allow for a constructive representation based on the pointwise maximum of random functions drawn from a Poisson point process defined on a suitable function space. Simulating from a max-id process is often difficult due to its complex stochastic structure, while calculating its joint density in high dimensions is often numerically infeasible. Therefore, exact and efficient simulation techniques for max-id processes are useful tools for studying the characteristics of the process and for drawing statistical inferences. Inspired by the simulation algorithms for max-stable processes, theory and algorithms to generalize simulation approaches tailored for certain flexible (existing or new) classes of max-id processes are presented. Efficient simulation for a large class of models can be achieved by implementing an adaptive rejection sampling scheme to sidestep a numerical integration step in the algorithm. The results of a simulation study highlight that our simulation algorithm works as expected and is highly accurate and efficient, such that it clearly outperforms customary approximate sampling schemes. As a by-product, new max-id models, which can be represented as pointwise maxima of general location-scale mixtures and possess flexible tail dependence structures capturing a wide range of asymptotic dependence scenarios, are also developed.