.png){kind=icon}
返回
Modelling across extremal dependence classes
.png){kind=icon}
OA
AI
DOI：10.1111/rssb.12157.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available statistical models suit one or other of these cases, but not both, resulting in a stage in the inference that is unaccounted for but can substantially impact subsequent extrapolation. Existing modelling solutions to this problem are either applicable only on subdomains or appeal to multiple limit theories. We introduce a unified representation for bivariate extremes that encompasses a wide variety of dependence scenarios and applies when at least one variable is large. Our representation motivates a parametric model that encompasses both dependence classes. We implement a simple version of this model and show that it performs well in a range of settings.
Keyword:
Asymptotic independence
Censored likelihood
Conditional extremes
Dependence modelling
Extreme value theory
Multivariate regular variation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
机构
引用论文
.png){kind=icon}
分享收藏Stability Study of a Tide Embankment Subjected to Sea Level Variations Using Centrifugal Model Tests
PREAI
分享收藏分享收藏
查看更多内容