FRG: Collaborative Research: Statistical Modeling and Inference of Vast Matrices for Complex Problems

FRG：协作研究：复杂问题的庞大矩阵的统计建模和推理

• 批准号: 1265203
• 负责人: Yazhen Wang
• 金额: $ 72.2万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1265203&HistoricalAwards=false
• 关键词: FRG; Collaborative; Research; Statistical; Modeling

Technological advances make it possible to collect and store large data with relatively low costs. As a result, scientific studies in a wide range of fields routinely generate a massive amount of data. Oftentimes, our ability to obtain measurements outpaces our ability to derive useful information from them. These pressing challenges serve as the ultimate motivation for the proposed research. In particular, this collaborative proposal presents novel research plans on the development of statistical theory and methodologies as well as computational techniques for a host of problems involving large matrices ranging from covariance matrices, volatility matrices, density matrices to relational matrices.The past few years have witnessed an explosion of data as a result of scientific and technological advances. As data storage cost continues to fall, the focal point on these big data has been transitioning inevitably from data management towards deriving actionable insights from them. There is a pressing need to respond to these challenges and understand the profound impact of large data on scientific research and knowledge discovery. The proposed research project deals with emerging problems that arise naturally at the frontier of a multitude of scientific and technological fields such as systems biology, high-frequency finance, and quantum computing among others. As a consequence, the proposed research effort will not only push forward the state-of-the-art of statistical understanding of large matrices, but also facilitate the advancement of various scientific fields and their embracement of the digital revolution.

技术的进步使得以相对较低的成本收集和存储大数据成为可能。因此，广泛领域的科学研究通常会产生大量的数据。通常，我们获得度量的能力超过了我们从中获取有用信息的能力。这些紧迫的挑战是本研究的最终动机。特别是，这个合作提案提出了新的研究计划，关于统计理论和方法的发展，以及涉及协方差矩阵、波动矩阵、密度矩阵到关系矩阵等大型矩阵的大量问题的计算技术。过去几年，由于科技进步，数据爆炸式增长。随着数据存储成本的持续下降，这些大数据的焦点已经不可避免地从数据管理转向从中获取可操作的见解。我们迫切需要应对这些挑战，并了解大数据对科学研究和知识发现的深远影响。拟议的研究项目涉及在系统生物学、高频金融和量子计算等众多科学和技术领域的前沿自然出现的新问题。因此，拟议的研究工作不仅将推动对大型矩阵的最新统计理解，而且还将促进各个科学领域的进步及其对数字革命的拥抱。