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Hidden Markov models: Estimation theory and economic applications
In this thesis, maximum likelihood estimation of hidden Markov models in several settings is investigated. Nonparametric estimation of state-dependent general mixtures and log-concave densities is discussed theoretically and algorithmically. Penalized estimation for parametric hidden Markov models c...
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| Format: | Dissertation |
| Langue: | anglais |
| Publié: |
Philipps-Universität Marburg
2016
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| Accès en ligne: | Texte intégral en PDF |
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| Résumé: | In this thesis, maximum likelihood estimation of hidden Markov models in several settings is investigated. Nonparametric estimation of state-dependent general mixtures and log-concave densities is discussed theoretically and algorithmically. Penalized estimation for parametric hidden Markov models comparing several penalty functions is studied. In addition, various models based on mixture models and hidden Markov models differing in dependency structure and the inclusion of covariables are applied to a set of panel data containing the GDP of several countries. |
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| Description matérielle: | 126 Seiten |
| DOI: | 10.17192/z2016.0120 |