CAREER: Hierarchical Models for Spatial Extremes

职业：空间极值的层次模型

• 批准号: 1752280
• 负责人: Benjamin Shaby
• 金额: $ 40万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1752280&HistoricalAwards=false
• 关键词: CAREER; Hierarchical; Models; Spatial; Extremes

Rare events can have crippling effects on economies, infrastructure, and human health and well being. But in order to make sound decisions, understanding how large the most severe events are likely to be is imperative. The PI will focus on developing statistical tools for understanding the spatial structure of the most extreme events. These new tools will improve on existing models because they will be both more realistic and more computationally tractable. The PI will also apply these tools to help scientists and policymakers study risks posed by severe environmental phenomena like inland floods, wildfires, and coastal storm surges. Furthermore, the PI will organize workshops to foster closer integration of statistical and Earth science research, as well as develop graduate courses and a textbook focused on modern statistical methods for Earth science.The PI will develop stochastic models for extreme events in space that are 1) flexible enough to transition across different classes of extremal dependence, and 2) permit inference through likelihood functions that can be computed for large datasets. It will accomplish these modeling goals by representing stochastic dependence relationships conditionally, which will induce desirable tail dependence properties and allow efficient inference through Markov chain Monte Carlo (MCMC). The first research component will develop sub-asymptotic models for spatial extremes using max-infinitely divisible (max-id) processes, a generalization of the limiting max-stable class of processes, based on a conditional representation. The second research component will develop sub-asymptotic spatial models for extremes based on scale mixtures of spatial Gaussian processes. The PI will conduct closely interwoven computational development and theoretical explication of the joint tail dependence that the proposed hierarchically specified max-id and scale mixture processes induce. Finally, the PI will apply these models to problems of high societal impact, such as extreme precipitation risk, wildfire susceptibility, and coastal storm surge exposure. The PI will enhance connections between extreme value statisticians and communities of climate and atmospheric scientists, mitigation researchers, and stakeholders, through 1) biannual international workshops on weather and climate extremes, 2) a Ph.D. level course in spatial statistics which will include new advances and applications of spatial extremes, and 3) writing the textbook Modern Statistics for Earth Scientists. The PI also will add modules on extremes to Penn State's Sustainable Climate Risk Management (SCRiM) summer school, and contribute to SCRiM's electronic resources and interactive teaching materials for educators, decision makers, underrepresented groups, and the general public. The PI will strengthen existing collaborations with government agencies which are responsible for communicating and mitigating risk to the public posed by extremal environment phenomena.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

罕见事件可能会对经济，基础设施以及人类健康和福祉产生残酷的影响。但是，为了做出明智的决定，必须了解最严重的事件可能是多么大。 PI将着重于开发统计工具，以理解最极端事件的空间结构。这些新工具将改善现有模型，因为它们将更加现实，而且可以在计算方面更具处理方式。 PI还将应用这些工具来帮助科学家和政策制定者研究由内陆洪水，野火和沿海风暴潮所带来的严重环境现象带来的风险。此外，PI将组织研讨会，以促进统计和地球科学研究的更紧密整合，并开发研究生课程和专注于地球科学的现代统计方法的教科书。PI将开发空间中极端事件的随机模型，该模型是1）足够灵活的超级依赖性，并且可以通过庞大的数据来实现跨越的范围。 它将通过有条件地表示随机依赖关系来实现这些建模目标，这将引起理想的尾部依赖性，并通过马尔可夫链蒙特卡洛（MCMC）有效地推断。第一个研究组件将使用最大限制（MAX-ID）过程（基于条件表示的最大最大稳定过程的概括）开发用于空间极端的亚物体模型。 第二个研究成分将基于空间高斯过程的规模混合物，为极端模型开发亚物质空间模型。 PI将对所提出的层次指定的最大ID和比例混合过程诱导的关节尾依赖性进行紧密相互交织的计算发展和理论解释。 最后，PI将将这些模型应用于高社会影响的问题，例如极端降水风险，野火敏感性和沿海风暴潮流暴露。 PI将通过1）关于天气和气候极端气候的双年一次国际研讨会，增强极值统计学家与气候和大气科学家，缓解研究人员和利益相关者之间的联系，2）博士学位。空间统计的水平课程将包括新的进步和空间极端的应用，以及3）为地球科学家编写教科书现代统计。 PI还将为宾夕法尼亚州立大学的可持续气候风险管理（SCRIM）暑期学校增加极端的模块，并为Scrim的电子资源和互动教学材料做出了贡献，为教育者，决策者，代表性不足的团体和普通大众提供了贡献。 PI将加强与政府机构的现有合作，这些合作负责向极端环境现象构成的公众沟通和减轻风险。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来评估的支持。