Iterative and recursive estimators for hidden Markov errors-in-variables models

TitleIterative and recursive estimators for hidden Markov errors-in-variables models
Publication TypeJournal Article
Year of Publication1996
AuthorsKrishnamurthy, V., and A. Logothetis
JournalSignal Processing, IEEE Transactions on
Volume44
Pagination629 -639
Date Publishedmar.
ISSN1053-587X
Keywordsapplications, communication systems, econometrics, error statistics, expectation maximization algorithm, finite-state Markovian disturbances, gradient-based scheme, hidden Markov errors-in-variables models, hidden Markov models, iterative estimators, iterative methods, Kullback-Leibler information measure, log likelihood, maximum likelihood estimation, maximum-likelihood estimation, neurobiological signal processing, on-line algorithm, on-line estimation schemes, recursive estimation, recursive estimators, signal processing, speech processing, stochastic approximations
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 ldquo;on-line rdquo; 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

URLhttp://dx.doi.org/10.1109/78.489036
DOI10.1109/78.489036

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