论文标题
使用集合Kalman滤波器的稳健参数估计
Robust parameter estimation using the ensemble Kalman filter
论文作者
论文摘要
用于随机微分方程的参数估计的标准最大似然或贝叶斯的方法对连续数据的扰动并不强大。在本文中，我们在使用集合卡尔曼滤波器的连续时间参数估计的背景下对这种观察进行了相当基本的解释。我们采用了常见的观点，可以对三种强大的估计技术发明新的启示。即对数据，粗糙路径校正和数据过滤进行次采样。我们通过简单的数值实验来说明我们的发现。
Standard maximum likelihood or Bayesian approaches to parameter estimation for stochastic differential equations are not robust to perturbations in the continuous-in-time data. In this paper, we give a rather elementary explanation of this observation in the context of continuous-time parameter estimation using an ensemble Kalman filter. We employ the frequentist perspective to shed new light on three robust estimation techniques; namely subsampling the data, rough path corrections, and data filtering. We illustrate our findings through a simple numerical experiment.
