Mathematical Sciences: Diagnostics in Structured Data and Quality Improvement

建议：DMS 95-05440 PI：道格拉斯·霍金斯研究所：明尼苏达大学标题：结构化数据中的诊断学和质量改进摘要：这项工作的主要方向是病例和模型诊断学，以及处理具有自然顺序的数据的方法，如时间序列，或按预测者之一排序的回归数据。这项工作的第一个分支扩展了在多元回归、多变量和时间序列数据中识别非典型案例的方法，以及缓和它们对随后数据分析的影响的方法。这项工作的第二个分支涉及确定有序数据(例如回归和多变量数据)的结构变化和克服未知参数值造成的困难的方法问题。这项工作的一个主要应用是统计质量改进。许多重要的社会问题涉及到从复杂的数据集做出有效的推论。例如，一个设施是否会增加地下水中的污染水平，是根据一系列地下水样本做出的。对这些序列的分析因存在明显的特立独行的值而变得复杂，这些值可能是实验室错误的结果，也可能反映了实际的污染事件。与该奖项一起开发的方法有助于承认这些价值并解决真实污染水平的问题。质量改进非常重要--无论是在制造业，还是在政府和私营部门的所有领域。通过确定一系列测量中的结构变化，可以找到劣化质量改进的指针。在这项研究中，强大、易用的变化诊断方法的发展将导致更合理和更可靠的方法来确定变化的根源。这项工作同时包含了方法论和计算两方面的内容。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发展也导致解决目前即使是最强大的计算机系统也无法解决的问题。

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.