The Annals of Statistics
- Ann. Statist.
- Volume 26, Number 4 (1998), 1614-1635.
Asymptotic normality of the maximum-likelihood estimator for general hidden Markov models
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.
Ann. Statist., Volume 26, Number 4 (1998), 1614-1635.
First available in Project Euclid: 21 June 2002
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Bickel, Peter J.; Ritov, Ya’acov; Rydén, Tobias. Asymptotic normality of the maximum-likelihood estimator for general hidden Markov models. Ann. Statist. 26 (1998), no. 4, 1614--1635. doi:10.1214/aos/1024691255. https://projecteuclid.org/euclid.aos/1024691255