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Efficient Iterated Filtering

Lindström, Erik LU orcid ; Ionides, Edward ; Frydendall, Jan and Madsen, Henrik (2012) 16th IFAC Symposium on System Identification p.1785-1790
Abstract
Parameter estimation in general state space models is not trivial as the likelihood is unknown. We propose a recursive estimator for general state space models, and show that the estimates converge to the true parameters with probability one. The estimates are also asymptotically Cramer-Rao efficient. The proposed estimator is easy to implement as it only relies on non-linear filtering. This makes the framework flexible as it is easy to tune the implementation to achieve computational efficiency. This is done by using the approximation of the score function derived from the theory on Iterative Filtering as a building block within the recursive maximum likelihood estimator.
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Recursive estimation, maximum likelihood estimator, filtering techniques, stochastic approximation, iterative methods
host publication
IFAC-PapersOnLine (System Identification, Volume 16)
pages
6 pages
publisher
Elsevier
conference name
16th IFAC Symposium on System Identification
conference location
Brussels, Belgium
conference dates
2012-07-11 - 2012-07-13
external identifiers
  • scopus:84867049749
ISBN
978-3-902823-06-9 (online)
DOI
10.3182/20120711-3-BE-2027.00300
language
English
LU publication?
yes
additional info
The paper is accessible (free of charge) to the public . To download the paper an account at IFAC is needed. The web browser Internet Explorer is recommended.
id
497a6486-6683-4929-b12f-27098379cb04 (old id 3008469)
date added to LUP
2016-04-04 12:12:46
date last changed
2022-01-29 23:03:30
@inproceedings{497a6486-6683-4929-b12f-27098379cb04,
  abstract     = {{Parameter estimation in general state space models is not trivial as the likelihood is unknown. We propose a recursive estimator for general state space models, and show that the estimates converge to the true parameters with probability one. The estimates are also asymptotically Cramer-Rao efficient. The proposed estimator is easy to implement as it only relies on non-linear filtering. This makes the framework flexible as it is easy to tune the implementation to achieve computational efficiency. This is done by using the approximation of the score function derived from the theory on Iterative Filtering as a building block within the recursive maximum likelihood estimator.}},
  author       = {{Lindström, Erik and Ionides, Edward and Frydendall, Jan and Madsen, Henrik}},
  booktitle    = {{IFAC-PapersOnLine (System Identification, Volume 16)}},
  isbn         = {{978-3-902823-06-9 (online)}},
  keywords     = {{Recursive estimation; maximum likelihood estimator; filtering techniques; stochastic approximation; iterative methods}},
  language     = {{eng}},
  pages        = {{1785--1790}},
  publisher    = {{Elsevier}},
  title        = {{Efficient Iterated Filtering}},
  url          = {{http://dx.doi.org/10.3182/20120711-3-BE-2027.00300}},
  doi          = {{10.3182/20120711-3-BE-2027.00300}},
  year         = {{2012}},
}