Robust parameter estimation using the ensemble Kalman filter
Numerical Analysis
2023-12-20 v4 Numerical Analysis
Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.2201.00611,
title = {Robust parameter estimation using the ensemble Kalman filter},
author = {Sebastian Reich},
journal= {arXiv preprint arXiv:2201.00611},
year = {2023}
}