Models and inference for uncertainty in extremal dependence
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AI 根据所提供的论文材料归类：摘要应用于海洋极值依赖，未明确涉及金融尾部风险或其他所列领域。
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极端依赖不确定性的模型与推断
- 研究问题
- 当有限阈值下存在依赖，却难以判定极限中是否仍然依赖时，如何把这种状态不确定性纳入多变量极值建模与外推？
- 方法与思路
- 作者构造使渐近依赖发生在参数空间边界上的多变量模型，通过贝叶斯推断与 MCMC 向该边界区域赋予后验概率质量。
- 材料陈述的贡献
- 摘要称方法可跨越渐近依赖与渐近独立两类模型进行推断，并以依赖状态难以判定的海洋观测数据展示其用途。
- 阅读关注点
- 应核对边界模型的参数化、先验如何赋予边界概率质量，以及依赖状态的不确定性如何传递到极端事件外推。
- 阅读边界
- 仅据版本未注明的索引摘要，无法判断先验敏感性或外推可靠性；海洋数据实例不能直接支持金融风险应用。
原文摘要
Conventionally, modelling of multivariate extremes has been based on the class of multivariate extreme value distributions. More recently, other classes have been developed, allowing for the possibility that, whilst dependence is observed at finite levels, the limit distribution is independent. A number of articles have shown this development to be important for accurate estimation of the extremal properties, both of theoretical processes and observed datasets. It has also been shown that, so far as dependence is concerned, the choice between modelling with either asymptotically dependent or asymptotically independent distributions can be far more influential than model choice within either of these two classes. In this paper we explore the issue of modelling across both classes, examining in particular the effect of uncertainty caused by lack of knowledge about the status of asymptotic dependence. This is achieved by new multivariate models whose parameter spaces are such that asymptotic dependence occurs on a boundary. Standard techniques in Bayesian inference, implemented through Markov chain Monte Carlo, enable inferences to be drawn that assign posterior probability mass to the boundary region. The techniques are illustrated on a set of oceanographic data for which previous analyses have shown that it is difficult to resolve the question of asymptotic dependence status, which is however important in model extrapolation.