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- W2161142726 abstract "In this paper we propose maximum-likelihood (ML) estimation of errors in variables models with finite-state Markovian disturbances. Such models have applications in econometrics, speech processing, communication systems, and neurobiological signal processing. We derive the maximum likelihood (ML) model estimates using the expectation maximization (EM) algorithm. Then two recursive or on-line estimation schemes are derived for estimating such models. The first on-line algorithm is based on the EM algorithm and uses stochastic approximations to maximize the Kullback-Leibler (KL) information measure. The second on-line algorithm we propose is a gradient-based scheme and uses stochastic approximations to maximize the log likelihood." @default.
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- W2161142726 title "Iterative and recursive estimators for hidden Markov errors-in-variables models" @default.
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- W2161142726 doi "https://doi.org/10.1109/78.489036" @default.
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