Sampling sup-normalized spectral functions for Brown-Resnick processes

被引：1

作者：

Oesting, Marco ^{[1
]}

Schlather, Martin ^{[2
]}

Schillings, Claudia ^{[2
]}

机构：

[1] Univ Siegen, Dept Math, Walter Flex Str 3, D-57072 Siegen, Germany

[2] Univ Mannheim, Inst Math, Mannheim, Germany

来源：

STAT | 2019年 / 8卷 / 01期

关键词：

Markov chain Monte Carlo; max-stable process; Pareto process; rejection sampling; spatial extremes; SIMULATION; PEAKS;

D O I：

10.1002/sta4.228

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Sup-normalized spectral functions form building blocks of max-stable and Pareto processes and therefore play an important role in modelling 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.

引用

页数：11

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