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

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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.