Statistical modeling and computations for data with network structure

网络结构数据的统计建模与计算

• 批准号: 1228164
• 负责人: Yves Atchade
• 金额: $ 45万
• 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: 2012-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1228164&HistoricalAwards=false
• 关键词: Statistical; modeling; computations; data; network

Recent advances in computing and measurement technologies have steadily placed network structures at the center of many disciplines in science and engineering. In many applications, researchers are dealing with multiple networks; either as the result of temporal evolution of the data generating process, or as the result of a mixture in the data generating process. This project aims to develop effective solutions to novel problems arising in the analysis of multiple and time evolving network structures. To accomplish this objective, (a) new statistical models are introduced, together with (b) a systematic investigation of the computational issues involved in estimating their dynamics and structures. Particular emphasis is placed on new theoretical techniques and computational tools for network problems. The research targets open problems in many fields, including biomedical and social science research, where network modeling and analysis plays an exceedingly important role.This research aims to develop statistical models and address related computational issues for large complex data sets with network structure that would aid researchers in the biomedical and social sciences to gain the necessary insights and form hypotheses to follow-up. Further, the developed methodology will be integrated into the curriculum of statistics at the UM and form a key component for dissertation projects. The results will be disseminated through an open software tool and presentations at conferences and workshops from the investigators.

计算和测量技术的最新进展已经将网络结构稳定地置于科学和工程中许多学科的中心。在许多应用中，研究人员正在处理多个网络;要么是数据生成过程的时间演变的结果，要么是数据生成过程中的混合结果。该项目旨在开发有效的解决方案，以分析多个和时间演变的网络结构中出现的新问题。为了实现这一目标，（a）新的统计模型介绍，连同（B）在估计其动态和结构的计算问题的系统调查。特别强调的是放在网络问题的新的理论技术和计算工具。本研究针对生物医学和社会科学研究等领域的开放性问题，在这些领域中，网络建模和分析发挥着极其重要的作用。本研究旨在为具有网络结构的大型复杂数据集开发统计模型并解决相关计算问题，以帮助生物医学和社会科学研究人员获得必要的见解并形成后续假设。此外，开发的方法将被整合到统计课程在UM和形成论文项目的关键组成部分。调查结果将通过一个开放式软件工具以及调查人员在会议和讲习班上的介绍加以传播。