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Consistency of the Maximum Likelihood Estimator for general hidden Markov models

Abstract : Consider a parametrized family of general hidden Markov models, where both the observed and unobserved components take values in a complete separable metric space. We prove that the maximum likelihood estimator (MLE) of the parameter is strongly consistent under a rather minimal set of assumptions. As special cases of our main result, we obtain consistency in a large class of nonlinear state space models, as well as general results on linear Gaussian state space models and finite state models. A novel aspect of our approach is an information-theoretic technique for proving identifiability, which does not require an explicit representation for the relative entropy rate. Our method of proof could therefore form a foundation for the investigation of MLE consistency in more general dependent and non-Markovian time series. Also of independent interest is a general concentration inequality for $V$-uniformly ergodic Markov chains.
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Preprints, Working Papers, ...
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Contributor : Eric Moulines Connect in order to contact the contributor
Submitted on : Tuesday, December 22, 2009 - 3:02:59 PM
Last modification on : Tuesday, December 8, 2020 - 9:48:20 AM
Long-term archiving on: : Friday, June 18, 2010 - 12:03:24 AM


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  • HAL Id : hal-00442774, version 1
  • ARXIV : 0912.4480


Randal Douc, Eric Moulines, Jimmy Olsson, Ramon van Handel. Consistency of the Maximum Likelihood Estimator for general hidden Markov models. {date}. ⟨hal-00442774⟩



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