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

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 在工程和质量控制中，以及作为模型的检测 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。

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.