Collaborative Research: Numerical algebra and statistical inference

合作研究：数值代数和统计推断

• 批准号: 1209155
• 负责人: Shayn Mukherjee
• 金额: $ 15万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1209155&HistoricalAwards=false
• 关键词: Collaborative; Research; Numerical; algebra; statistical

The investigators have two aims in this proposal that fall at the interface of numerical algebra and statistical inference. The first aim is to extend the use of randomized approximation in a variety of dimension reduction methods that rely on numerical linear algebra both supervised and unsupervised as well as linear and nonlinear and develop a statistical bases for these methods in addition to the computational motivation of being applicable to massive data. The other motivation is to extend these statistical methods for dimension reduction to multiway data using numerical multilinear algebra, a recent new development in numerical analysis. These projects will increase interaction between statistical inference and numerical analysis and benefit both fields, providing new perspectives to how we view and perform data analysis.Numerical methods with statistical implications are central to a variety of technologies used by the general population. These technologies include Google's pagerank algorithm, genetic methods used to find genetic variation related to disease, compressing of medical images for storage and treatment, as well as applications in geostatistics. In all the previous cases the fundamental idea is to condense massive data in a useful summary with respect to a desired goal. The two ideas in this proposal are (1) to study how numerical methods that scale to the massive data generated in modern scientific, engineering, and social applications impose statistical assumptions or models on the data, (2) to study more complex interactions or properties of the data than examined in current methods. The motivation behind the first aim is to understand how numerical approximations required for computational scaling as we collect more data impact the information that can be extracted from these data -- for what type of data and applications do certain numerical approximations work well. The motivation behind the second aim is to go beyond the broad category of standard statistical methods take into account the relation between pairs of objects -- two web pages that are linked for Google's pagerank, the correlation between two genes or two loci in genetics applications. The question behind this aim is whether richer sources of information can be extracted by examining the links between three web pages or three loci. The research involved in this aim consists of the development of computationally efficient algebraic methods to extract this information and understanding the statistical models implemented by these methods.

研究人员在该提案中有两个目标，这些目标属于数值代数和统计推断的界面。第一个目的是扩展在各种降低降低方法中使用随机近似值的使用，这些方法依赖于数值线性代数的监督和无监督，以及线性和非线性，并为这些方法开发统计基础，此外还适用于大量数据的计算动机。另一个动机是将这些统计方法扩展到使用数值多线性代数（数值分析的最新新发展），以将尺寸缩小降低到多路数据。这些项目将增加统计推断和数值分析之间的相互作用，并使这两个领域都受益，从而为我们如何看待和执行数据分析提供新的观点。具有统计意义的数量方法对于普通人群使用的各种技术至关重要。这些技术包括Google的Pagerank算法，用于查找与疾病相关的遗传变异的遗传方法，用于储存和治疗的医学图像以及在地统计学中的应用。在以前的所有情况下，基本想法是在有用的摘要中凝结大量数据。该提案中的两个想法是（1）研究如何扩展到现代科学，工程和社交应用中产生的大量数据的数值方法如何对数据施加统计假设或模型，（2）研究比当前方法中所检查的数据更复杂的相互作用或数据的属性。第一个目的背后的动机是了解计算缩放所需的数值近似如何在我们收集更多数据时会影响可以从这些数据中提取的信息 - 对于哪种类型的数据和应用程序，某些数值近似效果很好。第二个目标背后的动机是超越标准统计方法的广泛类别，考虑到对象对之间的关​​系 - 两个网页与Google的Pagerank链接在一起，这是两个基因或两个基因座之间的遗传应用中的相关性。此目标的背后问题是，是否可以通过检查三个网页或三个基因座之间的链接来提取更丰富的信息来源。此目标所涉及的研究包括开发计算有效的代数方法，以提取此信息并了解这些方法实施的统计模型。