Mathematical Sciences: Robust Recursive Procedures and Computationally Efficient Detection of Model Failures

数学科学：稳健的递归过程和模型故障的计算高效检测

• 批准号: 8706393
• 负责人: Roy Welsch
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-08-01 至 1990-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8706393&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Robust; Recursive; Procedures

This research will explore a broad class of recursive robust estimation procedures. Recursive procedures are important when it is too expensive to store all of the previous data and when real-time estimates are required. Update procedures can provide good approximate estimates at lower costs. The particular problem to be investigated relates to abrupt changes in parameters of static or dynamic models that arise in many areas. This type of problem is called jump, failure, or fault detection in engineering and in quality control, and as detection of model instability or change point analysis in statistics and econometrics. Much of the past work assumes an underlying Gaussian distribution. The principal investigator will explore more resistent recursive procedures.

本研究将探讨一个广泛的类递归鲁棒 估计程序。 递归过程在以下情况下很重要： 存储所有以前的数据太昂贵， 需要实时估计。 更新程序可以提供 以较低的成本进行良好的近似估计。 的特定 要研究的问题涉及到 静态或动态模型的参数，出现在许多领域。 这种类型的问题称为跳转、故障或故障检测 在工程和质量控制以及模型检测中 统计学中的不稳定性或变点分析 计量经济学 过去的许多工作都假设一个潜在的 高斯分布 首席研究员将探索 更有抵抗力的递归过程。