Workshop on: Discovery in Complex or Massive Datasets: Common Statistical Themes

研讨会：复杂或海量数据集中的发现：常见统计主题

• 批准号: 0751568
• 负责人: Jianqing Fan
• 金额: $ 4.55万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0751568&HistoricalAwards=false
• 关键词: Workshop; Discovery; Complex; Massive; Datasets

The amount and complexity of data generated to support contemporary science continues to grow rapidly. In domains from genomics to climate science, statisticians are actively engaged in interdisciplinary research teams. The history of statistics shows that, while frequently arising in response to challenges in specific scientific domains such as these, statistical methods and theory often achieve their full range and power from later application to subjects far remote from those of origin. The study of the ``intersections'' -- statistical problems, theories and methods that are relevant to multiple domains of scientific enquiry -- thus offers great opportunity in today's Age of Information.The proposed workshop responds to this opportunity: it aims to enumerate today's most intellectually compelling statistical challenges arising out of these intersections, in the hope of stimulating future research advances that will extend and enhance our data analytic toolkit for scientific discovery. The proceedings and discussions of the workshop will be distilled into a 10-15 page report, which will be disseminated widely by NSF through its website and other usual channels. This workshop and report respond to a number of compelling national needs: a) enhancing the nation's ``methodologic infrastructure'' for research, by advancing the capability of statistical theories and methods to contribute to discovery from massive and/or complex datasets, b) through promotion of research on statistical intersections, to advance the pre- and post- doctoral level training of research statisticians who will be critically needed core faculty members in expanding graduate and undergraduate degree programs in statistics in the U.S., c) fostering the flow of ideas between disciplines, as analysis methods developed in one domain are transferred in other areas.

为支持当代科学而产生的数据的数量和复杂性继续迅速增长。从基因组学到气候科学，统计学家积极参与跨学科的研究团队。统计的历史表明，虽然统计方法和理论经常是为了应对诸如此类的特定科学领域的挑战而产生的，但它们往往是在后来应用于远离其起源的主题时才充分发挥其范围和力量的。因此，对“交叉点”的研究——与科学探究的多个领域相关的统计问题、理论和方法——在今天的信息时代提供了巨大的机会。提议的研讨会回应了这一机会：它旨在列举当今从这些交叉点中产生的最具智力吸引力的统计挑战，希望能刺激未来的研究进展，从而扩展和增强我们的数据分析工具，用于科学发现。研讨会的会议记录和讨论将被提炼成一份10-15页的报告，由美国国家科学基金会通过其网站和其他常规渠道广泛传播。这个讲习班和报告回应了一些迫切的国家需要：a)通过提高统计理论和方法的能力来促进从大量和/或复杂数据集中发现的能力，加强国家的研究“方法基础设施”；b)通过促进统计交叉点的研究，推进研究统计学家的博士前和博士后水平的培训，这些统计学家将成为扩大美国统计研究生和本科学位课程所急需的核心教员；C)促进学科之间的思想交流，因为在一个领域开发的分析方法可以转移到其他领域。