Asymptotic normality of the maximum-likelihood estimator for general hidden Markov models
Bickel, Peter J. ; Ritov, Ya’acov ; Rydén, Tobias
Ann. Statist., Tome 26 (1998) no. 3, p. 1614-1635 / Harvested from Project Euclid
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Hidden Markov models (HMMs) have during the last decade become a widespread tool for modeling sequences of dependent random variables. Inference for such models is usually based on the maximum-likelihood estimator (MLE), and consistency of the MLE for general HMMs was recently proved by Leroux. In this paper we show that under mild conditions the MLE is also asymptotically normal and prove that the observed information matrix is a consistent estimator of the Fisher information.
Publié le : 1998-08-14
Classification:  Hidden Markov model,  incomplete data,  missing data,,  asymptotic normality,  62M09 
@article{1024691255,
     author = {Bickel, Peter J. and Ritov, Ya'acov and Ryd\'en, Tobias},
     title = {Asymptotic normality of the maximum-likelihood estimator for
		 general hidden Markov models},
     journal = {Ann. Statist.},
     volume = {26},
     number = {3},
     year = {1998},
     pages = { 1614-1635},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1024691255}
}

Bickel, Peter J.; Ritov, Ya’acov; Rydén, Tobias. Asymptotic normality of the maximum-likelihood estimator for
		 general hidden Markov models. Ann. Statist., Tome 26 (1998) no. 3, pp.  1614-1635. http://gdmltest.u-ga.fr/item/1024691255/