Standard maximum likelihood or Bayesian approaches to parameter estimation of stochastic differential equations are not robust to perturbations in the continuous-in-time data. In this note, 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 two robust estimation techniques; namely subsampling the data and rough path corrections. We illustrate our findings through a simple numerical experiment.
翻译:标准最大可能性或贝叶斯法对随机差分方程的参数估计方法对连续时间数据中的扰动作用不强。 在本说明中,我们在使用合用 Kalman 过滤器进行连续时间参数估计时对这一观察作了相当简单的解释。 我们从常客角度对两种稳健的估算技术,即对数据和粗略路径校正进行子取样,进行新的说明。 我们通过简单的数字实验来说明我们的结论。