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...
Сохранить в:
Главный автор: | |
---|---|
Другие авторы: | |
Формат: | Dissertation |
Язык: | английский |
Опубликовано: |
Philipps-Universität Marburg
2016
|
Предметы: | |
Online-ссылка: | PDF-полный текст |
Метки: |
Добавить метку
Нет меток, Требуется 1-ая метка записи!
|
Итог: | 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. |
---|---|
Объем: | 126 Seiten |
DOI: | 10.17192/z2016.0120 |