Development of Composite Likelihood Method in High-Dimensional Correlated Data Analysis: Estimation, Inference and Model Selection

该奖项是根据2009年《美国复苏和再投资法案》(Public Law Of 2009)(Public Law 111&amp；#8208；5)资助的。计算和测量技术的最新进展为主题科学家提供了发展大规模实验和雄心勃勃的信息收集计划的机会，这些计划导致了各种类型的高维相关数据。这项建议侧重于发展用于分析这种高维相关数据的复合似然统计理论和方法。特别是，主要研究人员计划实现三个研究目标：开发一种在复合似然理论背景下类似EM算法的新算法来分析不完整的高维数据；开发一种类似于贝叶斯信息准则的新的模型选择准则，用于模型参数数量可能增加的情况；以及开发一种新的程序来评估和提高复合似然方法中因降维而造成的效率损失。所有开发的方法都将应用于实际研究中的数据分析，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。这位首席研究员与密歇根大学生物学、计算机科学、流行病学、健康和医学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。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111‐5).Recent advances in computing and measurement technologies have given subject-matter scientists opportunities to develop large scale experiments and ambitious information collection schemes that have led to various types of high-dimensional correlated data. This proposal focuses on the development of statistical theory and methods of composite likelihood for analyzing such high-dimensional correlated data. In particular, the principle investigator plans to achieve three research goals: To develop a new algorithm analogous to the EM algorithm in the context of composite likelihood theory for the analysis of incomplete high-dimensional data; to develop a new model selection criterion analogous to the Bayesian Information Criterion for the scenario where the number of model parameters may increase in the sample size; and to develop a new procedure to evaluate and boost the efficiency loss due to dimension reduction in the composite likelihood methodology.All the developed methods will be applied to the analysis of data from practical studies to facilitate the understanding of subject-matter sciences and ultimately to improve human knowledge and quality of life. The principle investigator has close connections with researchers in other fields such as Biology, Computer Science, Epidemiology, Health and Medical Sciences 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.