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

本研究将探讨一类广泛的递归鲁棒估计程序。当存储所有以前的数据过于昂贵，并且需要实时估计时，递归过程非常重要。更新程序可以以较低的成本提供良好的近似估计。要研究的特殊问题涉及在许多地区出现的静态或动态模型参数的突变。这类问题在工程和质量控制中称为跳跃、故障或故障检测，在统计学和计量经济学中称为模型不稳定性检测或变点分析。过去的许多工作都假设了一个基本的高斯分布。首席研究员将探索更多的抗递归过程。

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.