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  Maximum likelihood estimation in hidden Markov models with inhomogeneous noise.

Diehn, M., Munk, A., & Rudolf, D. (2019). Maximum likelihood estimation in hidden Markov models with inhomogeneous noise. ESAIM: Probability and Statistics, 23, 492-523. doi:10.1051/ps/2018017.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-95E4-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-95E7-F
Genre: Journal Article

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 Creators:
Diehn, M., Author
Munk, A.1, Author              
Rudolf, D., Author
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1Research Group of Statistical Inverse-Problems in Biophysics, MPI for biophysical chemistry, Max Planck Society, ou_1113580              

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Free keywords: Inhomogeneous hidden Markov models; quasi-maximum likelihood estimation; strong consistency; robustness; asymptotic mean stationarity
 Abstract: We consider parameter estimation in finite hidden state space Markov models with time-dependent inhomogeneous noise, where the inhomogeneity vanishes sufficiently fast. Based on the concept of asymptotic mean stationary processes we prove that the maximum likelihood and a quasi-maximum likelihood estimator (QMLE) are strongly consistent. The computation of the QMLE ignores the inhomogeneity, hence, is much simpler and robust. The theory is motivated by an example from biophysics and applied to a Poisson- and linear Gaussian model.

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Language(s): eng - English
 Dates: 2019-08-07
 Publication Status: Published online
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 Rev. Method: Peer
 Identifiers: DOI: 10.1051/ps/2018017
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Title: ESAIM: Probability and Statistics
Source Genre: Journal
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Pages: - Volume / Issue: 23 Sequence Number: - Start / End Page: 492 - 523 Identifier: -