Item type:Doctoral Thesis, Open Access

Identification and estimation of hidden Markov models

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Date

2014-09-25

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Publisher

Philipps-Universität Marburg
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Abstract

In the current thesis several selected aspects of the two related latent class models; finite mixtures and hidden Markov models, were considered. The problem of calculating the MLE of a Gaussian mixture with Newton's method and the consistency of penalized MLE were investigated. A penalized MLE procedure for univariate Gaussian hidden Markov models was introduced and shown to be consistent. Furthermore, a result on identifiability of nonparametric hidden Markov models is derived.

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Dates
Created: 2014Issued: 2014-09-25Updated: 2014-09-25
DOI
https://doi.org/10.17192/z2014.0393
Faculty
Fachbereich Mathematik und Informatik
Publisher
Philipps-Universität Marburg
Language
eng
Data types
DoctoralThesis
Keywords
IdentifikationPoint estimationKonsistenzClassification and discrimination; cluster analysisconsistencySchätzungmaximum likelihoodmaximum likelihoodhidden Markov modelsmixture modelsidentificationhidden Markov ModelleEstimationNone of the above, but in this section
DFG-subjects
StatistikKonsistenzIdentifikationSchätzung
DDC-Numbers
510
License
https://rightsstatements.org/vocab/InC-NC/1.0/
URI
https://open.uni-marburg.de/handle/10.17192/z2014.0393
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Alexandrovich, Grigory (1059904624): Identification and estimation of hidden Markov models. : Philipps-Universität Marburg 2014-09-25. DOI: https://doi.org/10.17192/z2014.0393.

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