Composite Estimating Function Approaches to GeoCopula Models for Complex Spatially Correlated Data

复杂空间相关数据的 GeoCopula 模型的复合估计函数方法

• 批准号: 1208939
• 负责人: Peter Song
• 金额: $ 17万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208939&HistoricalAwards=false
• 关键词: Composite; Estimating; Function; Approaches; GeoCopula

Recent advances in technologies have given subject-matter scientists powerful tools to conduct large scale experiments and collect complex spatially correlated data. This proposal focuses on the development of statistical models and methods of composite likelihood for analyzing such high-dimensional complex data. In particular, the investigator plans to achieve three research goals: To develop a unified framework of multivariate copula models with flexible and desirable correlation structures for spatio-temporal data, spatial-clustered data and spatial-network data; to develop a feasible and computationally tractable estimation and inference methods as well as algorithms in the proposed models; and to establish the large-sample theory for the proposed composite estimating function (JCEF) estimator. All the proposed statistical models and methods will be applied to analyze data from practical studies to facilitate the understanding of subject-matter sciences and ultimately to improve human knowledge and quality of life. The investigator has close connections with researchers in other fields such as Epidemiology, Environmental Health Sciences, Infectious Diseases and Policy in Transportation Safety at University of Michigan. He has been working closely with these scientists who will serve as local users of the methodologies and provide valuable feedback. The project is also devoted to substantial educational initiatives that will involve undergraduate and graduate students and expose them to state-of-the-art research in various interdisciplinary topics related to the proposed research. These include new courses, short courses at major conferences, summer workshops, mentoring, and software development. These and other dissemination activities will increase awareness of modern powerful methods for data analysis among scientists from other fields.

最近技术的进步为主题科学家提供了进行大规模实验和收集复杂的空间相关数据的强大工具。这一建议的重点是发展统计模型和复合似然方法，以分析这种高维复杂数据。特别是，研究人员计划实现三个研究目标：为时空数据、空间集群数据和空间网络数据开发一个具有灵活和理想的相关性结构的多变量Copula模型的统一框架；开发一种可行的、计算上容易处理的估计和推理方法以及所提出的模型中的算法；以及为所提出的综合估计函数(JCEF)估计器建立大样本理论。所有拟议的统计模型和方法都将用于分析来自实际研究的数据，以促进对主题科学的理解，并最终提高人类的知识和生活质量。这位调查员与密歇根大学流行病学、环境健康科学、传染病和交通安全政策等其他领域的研究人员关系密切。他一直与这些科学家密切合作，这些科学家将作为这些方法的当地用户并提供宝贵的反馈。该项目还致力于实质性的教育倡议，将涉及本科生和研究生，并使他们接触到与拟议研究相关的各种跨学科主题的最新研究。这些课程包括新课程、大型会议的短期课程、暑期研讨会、指导和软件开发。这些活动和其他传播活动将提高其他领域的科学家对现代强有力的数据分析方法的认识。