Mathematical Sciences: Diagnostics and Graphics for Structured Data

数学科学：结构化数据的诊断和图形

• 批准号: 9208819
• 负责人: Douglas Hawkins
• 金额: $ 5.5万
• 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: 1992
• 资助国家: 美国
• 起止时间: 1992-08-15 至 1995-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9208819&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Diagnostics; Graphics; Structured

This project will develop numeric measures and real-time graphical procedures for diagnostic use with regression models, generalized linear models, multivariate data and other structured data. Graphical methods will combine parametric estimation and kernel smoothers to make these methods useful and intuitively sensible beyond the linear model case. Numeric case diagnostics will be based on multiple subsetting of the data to extend the sensitivity to outliers beyond the single outlier-simple random sample case. When analyzing data by fitting models, one of the most important tasks is to check for failures of the model to describe the behavior of the data. For linear models, for example multiple linear regression models, both numerical and graphical diagnostics successfully identify aberrant individual observations or "outliers." This research will develop diagnostics for the much larger group of models which are used appropriately for more highly structured or more complicated situations.

该项目将开发数字测量和实时 用于回归模型诊断的图形程序， 广义线性模型、多变量数据和其他结构化 数据 图形方法将结合联合收割机参数估计和 内核平滑器，使这些方法有用和直观 超越线性模型的情况。 数字案例诊断 将基于数据的多个子集来扩展 对单个异常值以外的异常值的敏感性-简单随机 示例案例。 当通过拟合模型分析数据时， 一个重要的任务是检查模型的故障描述 数据的行为。 对于线性模型，例如 多元线性回归模型，包括数值和图形 诊断成功识别异常个体 观察值或“离群值”。“这项研究将发展 对所使用的更大一组模型进行诊断 适用于更高度结构化或更复杂的 situations.