论文标题
检测因果协变量的极端依赖性结构
Detecting causal covariates for extreme dependence structures
论文作者
论文摘要
在许多科学领域，确定极端事件的原因是一个基本问题。建模多元极端时的一个重要方面是尾部依赖性。在应用中，极端依赖结构可能显着取决于协变量。至于包括协变量在内的一般建模情况，只有一些协变量是因果关系。在本文中，我们提出了一种方法来发现因果协变量，解释了两个变量之间的尾巴依赖性结构。提出的发现因果变量的方法基于比较来自不同环境或扰动的观察结果。这是一种预期在新的未观察到的环境中预测极端行为的方法。该方法应用于英国的$ \ text {no} _2 $浓度的数据集。极端$ \ text {no} _2 $级别可能会导致严重的健康问题，并且了解并发严重水平的行为是一个重要的问题。我们专注于揭示因$ \ text {no} _2 $观测值之间依赖性的因果预测指标。
Determining the causes of extreme events is a fundamental question in many scientific fields. An important aspect when modelling multivariate extremes is the tail dependence. In application, the extreme dependence structure may significantly depend on covariates. As for the general case of modelling including covariates, only some of the covariates are causal. In this paper, we propose a methodology to discover the causal covariates explaining the tail dependence structure between two variables. The proposed methodology for discovering causal variables is based on comparing observations from different environments or perturbations. It is a desired methodology for predicting extremal behaviour in a new, unobserved environment. The methodology is applied to a dataset of $\text{NO}_2$ concentration in the UK. Extreme $\text{NO}_2$ levels can cause severe health problems, and understanding the behaviour of concurrent severe levels is an important question. We focus on revealing causal predictors for the dependence between extreme $\text{NO}_2$ observations at different sites.
