On recursive estimation for hidden Markov models
Hidden Markov models (HMMs) have during the last decade become a widespread tool for modelling sequences of dependent random variables. In this paper we consider a recursive estimator for HMMs based on the m-dimensional distribution of the process and show that this estimator converges to the set of stationary points of the corresponding Kullback-Leibler information. We also investigate averaging in this recursive scheme and show that conditional on convergence to the true parameter, and provided m is chosen large enough, the averaged estimator is close to optimal.
Year of publication: |
1997
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Authors: | Rydén, Tobias |
Published in: |
Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 66.1997, 1, p. 79-96
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Publisher: |
Elsevier |
Keywords: | Hidden Markov model Incomplete data Missing data Recursive estimation Stochastic approximation Poisson equation |
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