Structured extreme value models for environmental science.

环境科学的结构化极值模型。

• 批准号: 2753504
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2753504
• 关键词: Structured; extreme; value; models; environmental

Environmental processes such as precipitation, temperature or wind can have devastating consequences, i.e., floods, drought, or wildfires. These extreme events can have severe impact on society, but they are quite rare, which means that making inference about them is complex. My work uses a particular type of statistical methodology based on extreme value theory. The focus is on statistical modelling of both multivariate and spatial-temporal temporal extreme value problems. I will look to improve on existing methods, which typically require a large number of parameters, by making pragmatic and physically-motivated simplifying assumptions about the underlying structure of the extremal dependence. The type of assumptions I will look to make includes graphical model structure, vines, clustering and mixtures.In the multivariate setting, the aim is to develop generic methods and to explore their implementation to air pollutant data. For example, when multiple different chemicals are of interest, some chemicals are more directly related than others given they have common pollutant sources. In the spatial-temporal analysis, I will investigate adapting existing methods to very large spatial-scale studies, e.g., rainfall or temperature over the whole of Europe, which can have multiple separate extreme events occurring simultaneously; a feature that is not possible to describe in currently used models. The outcome of the research will be improved modelling and understanding of the extremal characteristics of a range of natural hazards.In partnership with Oslo University.

降水、温度或风等环境过程可能产生破坏性后果，即，洪水干旱或野火这些极端事件可能对社会产生严重影响，但它们非常罕见，这意味着对它们进行推断是复杂的。我的工作使用了一种基于极值理论的特殊类型的统计方法。重点是多变量和时空时间极值问题的统计建模。我将寻求改进现有的方法，通常需要大量的参数，通过对极值依赖的底层结构进行务实和物理动机的简化假设。假设的类型包括图形模型结构，葡萄树，聚类和混合物。在多变量环境中，目的是开发通用方法，并探索其在空气污染物数据中的应用。例如，当多种不同的化学品受到关注时，某些化学品比其他化学品更直接相关，因为它们具有共同的污染源。在时空分析中，我将研究使现有方法适应非常大的空间尺度研究，例如，整个欧洲的降雨或温度，可能有多个单独的极端事件同时发生;这是目前使用的模型无法描述的特征。这项研究的成果将是与奥斯陆大学合作，改进对一系列自然灾害极端特征的建模和理解。