# Frequentist perspective on robust parameter estimation using the ensemble Kalman filter

@article{Reich2022FrequentistPO, title={Frequentist perspective on robust parameter estimation using the ensemble Kalman filter}, author={Sebastian Reich}, journal={ArXiv}, year={2022}, volume={abs/2201.00611} }

. 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 two robust estimation techniques; namely subsampling the data and rough path corrections. We…

## 2 Citations

### Dimension free non-asymptotic bounds on the accuracy of high dimensional Laplace approximation

- Computer Science, MathematicsArXiv
- 2022

The classical results on Laplace approximation in a modern non-asymptotic and dimension free form are revisited and the issue of using a Gaussian approximation with inexact parameters with the focus on replacing the Maximum a Posteriori (MAP) value by the posterior mean is addressed.

### Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering

- MathematicsArXiv
- 2021

A McKean-Vlasov equation that contains the data stream as a common driving rough path is studied, establishing propagation of chaos for the associated interacting particle system, which is suggestive of a numerical scheme that can be viewed as an extension of the ensemble Kalman filter to a rough-path framework.

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