Mathematical Sciences: Diagnostics in Structured Data and Quality Improvement

数学科学：结构化数据诊断和质量改进

• 批准号: 9505440
• 负责人: Douglas Hawkins
• 金额: $ 11.4万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-07-01 至 1999-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9505440&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Diagnostics; Structured; Data

Proposal: DMS 95-05440 PI: Douglas Hawkins Institution: University of Minnesota Title: Diagnostics in Structured Data and Quality Improvement Abstract: The principal directions of the work are case and model diagnostics, and methdologies for handling data with a natural ordering such as time series, or regression data ordered by one of the predictors. The first branch of the work extends methods for identifying atypical cases in multiple regression, multivariate and time series data, and for moderating their effects on the subsequent analysis of the data. The second branch of the work addresses problems of methodologies for identifying structural changes in ordered data (for example regression and multivariate data) and for overcoming the difficulties caused by unknown parameter values. A major application for this work is statistical quality improvement. Many important societal problems involve making valid inferences from complex data sets. The decision whether a facility is adding to the pollution levels in the groundwater, for example, is made on the basis of series of groundwater samples. Analyzing these series is complicated by the presence of apparent maverick values, which may be the result of laboratory error or may reflect actual pollution events. The methods developed with this award help to recognize such values and to resolve the issue of the true pollution levels. Quality improvement is of great importance - both in manufacturing and in all areas of government and the private sector. Pointers to quality improvement for deterioration can be found from identifying structural changes in series of measurements The development of powerful easily-used change diagnostics in this research will lead to sounder and more reliable ways of pinpointing the sources of change. The work has both methodological and computational elements. While the computational aspect benefits from high performance computing equipment and me thods, the development of better computational procedures also leads to solutions to problems currently beyond the powers of even the most powerful computer systems.

题目：结构化数据诊断和质量改进摘要：本研究的主要方向是病例和模型诊断，以及处理自然排序数据的方法，如时间序列，或由一个预测因子排序的回归数据。该工作的第一个分支扩展了在多元回归、多元和时间序列数据中识别非典型病例的方法，并缓和了它们对数据后续分析的影响。工作的第二个分支涉及识别有序数据（例如回归和多变量数据）中的结构变化的方法问题，以及克服未知参数值引起的困难。这项工作的一个主要应用是统计质量的改进。许多重要的社会问题都涉及到从复杂的数据集中做出有效的推断。例如，一个设施是否会增加地下水的污染水平，是根据一系列地下水样本做出的决定。由于存在明显的异常值，分析这些序列变得复杂，这些异常值可能是实验室误差的结果，也可能反映了实际的污染事件。该奖项所开发的方法有助于认识这些价值，并解决真实污染水平的问题。无论是在制造业，还是在政府和私营部门的所有领域，质量改进都是非常重要的。在本研究中，通过识别一系列测量中的结构变化，可以找到质量改进的指针。在本研究中，开发功能强大且易于使用的变化诊断方法将导致更健全、更可靠的方法来确定变化的来源。这项工作既有方法论方面的，也有计算方面的。虽然计算方面受益于高性能计算设备和方法，但更好的计算过程的发展也导致了目前甚至是最强大的计算机系统也无法解决的问题的解决方案。