Bernoulli

  • Bernoulli
  • Volume 2, Number 3 (1996), 199-228.

Inference in hidden Markov models I: Local asymptotic normality in the stationary case

Peter J. Bickel and Ya'acov Ritov

Full-text: Open access

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.

Article information

Source
Bernoulli, Volume 2, Number 3 (1996), 199-228.

Dates
First available in Project Euclid: 4 May 2007

Permanent link to this document
https://projecteuclid.org/euclid.bj/1178291719

Digital Object Identifier
doi:10.3150/bj/1178291719

Mathematical Reviews number (MathSciNet)
MR1416863

Zentralblatt MATH identifier
1066.62535

Keywords
geometric ergodicity hidden Markov models local asymptotic normality maximum likelihood

Citation

Bickel, Peter J.; Ritov, Ya'acov. Inference in hidden Markov models I: Local asymptotic normality in the stationary case. Bernoulli 2 (1996), no. 3, 199--228. doi:10.3150/bj/1178291719. https://projecteuclid.org/euclid.bj/1178291719


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