Diagnostics for structured data and quality improvement

结构化数据诊断和质量改进

• 批准号: 0306304
• 负责人: Douglas Hawkins
• 金额: $ 15.61万
• 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: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0306304&HistoricalAwards=false
• 关键词: Diagnostics; structured; data; quality; improvement

DMS-0306304Diagnostics for Structured Data and Quality ImprovementDouglas M. Hawkins, University of MinnesotaAbstractThe investigator and his collaborators and thesis advisees develop diagnostic methods for a variety of problems linked by their use of data having structure beyond that of a univariate random sample. Examples include the identification of outliers and unexpected parameter changes in multiple regression, multivariate time series data, and large rectangular data arrays. Application of these methodologies to statistical quality improvement resolves many deficiencies of current approaches and provides high performance charting methods.Data sets keep growing in size and complexity. Proteomics and microarray experiments routine provide data sets of a size scarcely imaginable a decade ago. This creates a need for sturdy statistical methods to extract information without manual checking and in the face of a fraction of erroneous or missing readings. The research addresses these needs, by developing appropriate statistical methods and effective ways of implementing them. At the other end of the size spectrum, just-in-time and the ability to customize in manufacturing create a need for statistical quality control tools able to react quickly, and to relatively small departures from specification. Change-point methodologies provide sharp tools for this detection and diagnosis.

DMS-0306304结构化数据和质量诊断Douglas M.霍金斯，明尼苏达大学摘要研究者和他的合作者以及论文作者为各种问题开发了诊断方法，这些问题通过使用具有超出单变量随机样本结构的数据而联系在一起。 例子包括在多元回归、多变量时间序列数据和大型矩形数据阵列中识别异常值和意外参数变化。 将这些方法应用于统计质量改进，解决了现有方法的许多不足，并提供了高性能的图表方法。 蛋白质组学和微阵列实验常规提供的数据集的大小在十年前几乎无法想象。 这就需要可靠的统计方法来提取信息，而无需手动检查，并且面对一小部分错误或遗漏的读数。 该研究通过开发适当的统计方法和有效的实施方法来满足这些需求。 在尺寸范围的另一端，及时性和制造定制的能力需要能够快速反应的统计质量控制工具，并且与规格的偏差相对较小。 变点方法为这种检测和诊断提供了锐利的工具。