Consistent and Asymptotically Normal Parameter Estimates for Hidden Markov Models
Tobias Rydén · The Annals of Statistics · 1994
Hidden Markov models are today widespread for modeling of various phenomena. It has recently been shown by Leroux that the maximum-likelihood estimate (MLE) of the parameters of a such a model is consistent, and local asymptotic normality has been proved by Bickel and Ritov. In this paper we propose a new class of estimates which are consistent, asymptotically normal and almost as good as the MLE.