From Approximate to Exact Designs with Applications to Big Data

从近似设计到精确设计以及应用程序和大数据

• 批准号: 1612978
• 负责人: Wei Zheng
• 金额: $ 15万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1612978&HistoricalAwards=false
• 关键词: Approximate; Exact; Designs; Applications; Big

Design of experiments is an integral part of the scientific process in many areas of research with a direct impact on society, such as the biological sciences, the health sciences, the social sciences, engineering, marketing, and education. A well-chosen design facilitates the collection of data that, at a minimum cost, maximizes the information for the scientific questions of interest. Many scientific studies allow for repeated use of conceptual units, so that developing tools for optimal design for these problems has great potential impact. Particularly in the realm of big data, there is much room for improvement of existing methods for design of experiments, and the tools and concepts under development in this research project have potential to lead to significant gain of information without increasing computational cost. Results from the project will be made available to researchers in other areas through easy-to-use software that implements the algorithms to be developed. Graduate students will be trained to become researchers in design of experiments. This project aims to result in a major leap forward in understanding and knowledge of optimal design of experiments. Recent work in the field has had a significant impact on the advancement of optimal crossover designs and designs for interference models for arbitrarily given covariance structures and design size configurations. However, these results have for the most part been limited to approximate designs for relatively simple models. While these results are arguably important in their own right, this project will extend methods and tools to achieve the ultimate goal of deriving exact designs for a wider spectrum of practical models. The results will be a much needed addition to our collective design toolbox. Most importantly, this project will go beyond the territory of design and apply the tools and ideas from design of experiments to subsampling problems emerging in big data with both statistical and machine learning methods under consideration. Preliminary results indicate that this is an opportune time to make these challenging but critical steps.

实验设计是许多研究领域的科学过程的一个组成部分，对社会有直接影响，如生物科学，健康科学，社会科学，工程，营销和教育。 精心挑选的设计有助于收集数据，以最低的成本，最大限度地为感兴趣的科学问题提供信息。 许多科学研究允许重复使用概念单元，因此为这些问题开发优化设计工具具有很大的潜在影响。 特别是在大数据领域，现有的实验设计方法还有很大的改进空间，本研究项目正在开发的工具和概念有可能在不增加计算成本的情况下获得大量信息。 该项目的结果将通过易于使用的软件提供给其他领域的研究人员，该软件实现了有待开发的算法。研究生将被培养成为实验设计的研究人员。该项目的目的是在理解和知识的实验优化设计的一个重大飞跃。最近在该领域的工作有显着的影响，对任意给定的协方差结构和设计尺寸配置的最佳交叉设计和干扰模型的设计的进步。然而，这些结果在很大程度上仅限于相对简单模型的近似设计。虽然这些结果本身就很重要，但该项目将扩展方法和工具，以实现为更广泛的实用模型导出精确设计的最终目标。结果将是我们集体设计工具箱中急需的补充。最重要的是，该项目将超越设计领域，将实验设计的工具和想法应用于大数据中出现的子采样问题，同时考虑统计和机器学习方法。初步结果表明，这是采取这些具有挑战性但至关重要的步骤的适当时机。