Asymptotic properties of the maximum likelihood estimation in misspecified hidden Markov models

Abstract

Let $(Y_k{)}_{k\in \mathbb{Z}}$ be a stationary sequence on a probability space $(\Omega ,\mathcal{A},\mathbb{P})$ taking values in a standard Borel space $\mathsf{Y}$. Consider the associated maximum likelihood estimator with respect to a parametrized family of hidden Markov models such that the law of the observations $(Y_k{)}_{k\in \mathbb{Z}}$ is not assumed to be described by any of the hidden Markov models of this family. In this paper we investigate the consistency of this estimator in such misspecified models under mild assumptions.

Published in at http://dx.doi.org/10.1214/12-AOS1047 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (1)

• On some problems in the article "Efficient Likelihood Estimation in State Space Models" by Cheng-Der Fuh

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• Stability of Conditional Sequential Monte Carlo
• On the Robustness of Mixture Models in the Presence of Hidden Markov Regimes with Covariate-Dependent Transition Probabilities
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