Work

Conditional Sampling for Spectrally Discrete Max-Stable Random Fields

Year: 2010

Type: preprint

Abstract: Max-stable random fields play a central role in modeling extreme value phenomena. We obtain an explicit formula for the conditional probability in general max-linear models, which include a large class of max-stable random fields. As a consequence, we develop an algorithm for efficient and exact sampling from the conditional distributions. Our method provides a computational solution to the prediction problem for spectrally discrete max-stable random fields. This work offers new tools and a new perspective to many statistical inference problems for spatial extremes, arising, for example, in meteorology, geology, and environmental applications.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1005.0312

Authors: Wang, Yizao, Stoev, Stilian A.

Institution: University of Michigan

Language: English

Topic: Probability and Risk Models

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funder: National Science Foundation

Award: Extremes: Short and Long-Range Dependence; Modeling and Inference with Applications to Computer Networks and Risk Analysis

OpenAlex ID: W4307606532

Cites:

Cited by: 5

Related to: 10