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.

提案：DMS 95-05440 PI： 道格拉斯霍金斯 明尼苏达大学University of Minnesota 职务名称： 结构化数据的诊断与质量改进 摘要： 工作的主要方向是案例和模型诊断，以及用于处理具有自然排序的数据的方法，例如时间序列或由预测器之一排序的回归数据。 第一个分支的工作扩展的方法，识别非典型病例的多元回归，多变量和时间序列数据，并缓和其对随后的数据分析的影响。 第二个分支的工作解决的问题的方法，用于确定有序数据（例如回归和多元数据）的结构变化，并克服未知参数值所造成的困难。 这项工作的一个主要应用是提高统计质量。 许多重要的社会问题涉及从复杂的数据集做出有效的推断。 设施点是否添加到 例如，地下水的污染程度是根据 一系列的地下水样本 分析这些系列是复杂的，因为存在明显的标新立异值，这可能是实验室误差的结果，也可能反映实际的污染事件。 的方法 与此奖项一起开发的帮助认识到这些价值，并解决真正的污染水平问题。 质量改善是 这对制造业以及政府和私营部门的所有领域都非常重要。 质量改进的指标 在本研究中，开发出功能强大、易于使用的变化诊断方法，将为查明变化来源提供更可靠、更合理的方法。 工作既有 方法和计算元素。 虽然计算方面受益于高性能计算设备和方法，但更好的计算程序的开发也导致 解决目前甚至最强大的计算机系统都无法解决的问题。