{
  "id": "2201.00611",
  "version": "v2",
  "published": "2022-01-03T12:56:27.000Z",
  "updated": "2022-04-28T09:59:37.000Z",
  "title": "Frequentist perspective on robust parameter estimation using the ensemble Kalman filter",
  "authors": [
    "Sebastian Reich"
  ],
  "categories": [
    "math.NA",
    "cs.NA",
    "math.ST",
    "stat.TH"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2022-04-28T09:59:37.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "62L12",
      "62M20",
      "62F15",
      "60L90"
    ],
    "keywords": [
      "ensemble kalman filter",
      "robust parameter estimation",
      "frequentist perspective",
      "standard maximum likelihood",
      "stochastic differential equations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}