A generalization of the Kalman filter to models with infinite variance
The problem of optimal linear estimation for continuous time processes is investigated. The signal and observation processes are solutions of a linear system. The optimal filter is given by recursive equations which reduce to the classical Kalman-Bucy equations when the system is driven by independent white noises. The filter is defined by a left innovations process. Solutions to the prediction and smoothing problems are obtained. The assumptions concerning the errors allow to consider models with infinite variance.
Year of publication: |
1993
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Authors: | Le Breton, Alain ; Musiela, Marek |
Published in: |
Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 47.1993, 1, p. 75-94
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Publisher: |
Elsevier |
Subject: | optimal linear filtering | prediction and smoothing semimartingales with infinite variance metric projection James' orthogonality left innovations |
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