The Annals of Statistics
- Ann. Statist.
- Volume 22, Number 4 (1994), 1884-1895.
Consistent and Asymptotically Normal Parameter Estimates for Hidden Markov Models
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
