Publication: Research - peer-review › Conference abstract in proceedings – Annual report year: 2012
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.
|Publisher||International Federation of Automatic Control|
|Conference||16th IFAC Symposium on System Identification|
|Period||11/07/12 → 13/07/12|
|Name||IFAC Proceedings Volumes (IFAC-PapersOnline)|
|Citations||Web of Science® Times Cited: No match on DOI|
- Approximation theory, Iterative methods, Maximum likelihood estimation, Parameter estimation, State space methods, Estimation
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