The Annals of Statistics

Consistent and Asymptotically Normal Parameter Estimates for Hidden Markov Models

Tobias Ryden

Full-text: Open access

Abstract

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.

Article information

Source
Ann. Statist., Volume 22, Number 4 (1994), 1884-1895.

Dates
First available in Project Euclid: 11 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176325762

Digital Object Identifier
doi:10.1214/aos/1176325762

Mathematical Reviews number (MathSciNet)
MR1329173

Zentralblatt MATH identifier
0831.62060

JSTOR
links.jstor.org

Subjects
Primary: 62M09: Non-Markovian processes: estimation
Secondary: 62F12: Asymptotic properties of estimators 62E25

Keywords
Hidden Markov model consistency asymptotic normality identifiability regenerative process

Citation

Ryden, Tobias. Consistent and Asymptotically Normal Parameter Estimates for Hidden Markov Models. Ann. Statist. 22 (1994), no. 4, 1884--1895. doi:10.1214/aos/1176325762. https://projecteuclid.org/euclid.aos/1176325762


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