Hidden Markov models: Estimation theory and economic applications

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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1. Verfasser: Leister, Anna Maria
Beteiligte: Holzmann, Hajo (Prof. Dr.) (BetreuerIn (Doktorarbeit))
Format: Dissertation
Sprache:Englisch
Veröffentlicht: Philipps-Universität Marburg 2016
Schlagworte:
Mathematik
EM-algorithm
Hidden Markov Modell
Schätztheorie
Maximum Likelihood Schätzung
EM-Algorithmus
Statistik
estimation theory
statistics
Hidden Markov models
Mathematics
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Beschreibung
Zusammenfassung: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.
Umfang:126 Seiten
DOI:10.17192/z2016.0120

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