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September 1996 Inference in hidden Markov models I: Local asymptotic normality in the stationary case
Peter J. Bickel, Ya'acov Ritov
Bernoulli 2(3): 199-228 (September 1996). DOI: 10.3150/bj/1178291719

Abstract

Following up on work by Baum and Petrie published 30 years ago, we study likelihood-based methods in hidden Markov models, where the hiding mechanism can lead to continuous observations and is itself governed by a parametric model. We show that procedures essentially equivalent to maximum likelihood estimates are asymptotically normal as expected and consistent estimates of the variance can be constructed, so that the usual inferential procedures are asymptotically valid.

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Peter J. Bickel. Ya'acov Ritov. "Inference in hidden Markov models I: Local asymptotic normality in the stationary case." Bernoulli 2 (3) 199 - 228, September 1996. https://doi.org/10.3150/bj/1178291719

Information

Published: September 1996
First available in Project Euclid: 4 May 2007

zbMATH: 1066.62535
MathSciNet: MR1416863
Digital Object Identifier: 10.3150/bj/1178291719

Keywords: geometric ergodicity , Hidden Markov models , local asymptotic normality , maximum likelihood

Rights: Copyright © 1996 Bernoulli Society for Mathematical Statistics and Probability

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Vol.2 • No. 3 • September 1996
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