Sammanfattning
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.
Originalspråk | engelska |
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Titel på värdpublikation | IFAC-PapersOnLine (System Identification, Volume 16) |
Förlag | Elsevier |
Sidor | 1785-1790 |
Antal sidor | 6 |
ISBN (tryckt) | 978-3-902823-06-9 (online) |
DOI | |
Status | Published - 2012 |
Evenemang | 16th IFAC Symposium on System Identification - Brussels, Belgien Varaktighet: 2012 juli 11 → 2012 juli 13 |
Konferens
Konferens | 16th IFAC Symposium on System Identification |
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Land/Territorium | Belgien |
Ort | Brussels |
Period | 2012/07/11 → 2012/07/13 |
Bibliografisk information
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