  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.
