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Asymptotic normality of M-estimators in nonhomogeneous hidden Markov models
Published online by Cambridge University Press: 14 July 2016
Abstract
Results on asymptotic normality for the maximum likelihood estimate in hidden Markov models are extended in two directions. The stationarity assumption is relaxed, which allows for a covariate process influencing the hidden Markov process. Furthermore, a class of estimating equations is considered instead of the maximum likelihood estimate. The basic ingredients are mixing properties of the process and a general central limit theorem for weakly dependent variables.
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- Type
- Part 6. Statistics
- Information
- Journal of Applied Probability , Volume 48 , Issue A: New Frontiers in Applied Probability (Journal of Applied Probability Special Volume 48A) , August 2011 , pp. 295 - 306
- Copyright
- Copyright © Applied Probability Trust 2011
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