Sampling Sup-Normalized Spectral Functions for Brown-Resnick Processes
Statistics Theory
2019-02-26 v1 Methodology
Statistics Theory
Abstract
Sup-normalized spectral functions form building blocks of max-stable and Pareto processes and therefore play an important role in modeling spatial extremes. For one of the most popular examples, the Brown-Resnick process, simulation is not straightforward. In this paper, we generalize two approaches for simulation via Markov Chain Monte Carlo methods and rejection sampling by introducing new classes of proposal densities. In both cases, we provide an optimal choice of the proposal density with respect to sampling efficiency. The performance of the procedures is demonstrated in an example.
Cite
@article{arxiv.1902.09230,
title = {Sampling Sup-Normalized Spectral Functions for Brown-Resnick Processes},
author = {Marco Oesting and Martin Schlather and Claudia Schillings},
journal= {arXiv preprint arXiv:1902.09230},
year = {2019}
}
Comments
11 pages, 2 figures