Mixtures of Nonparametric Autoregression, revised
- We consider data generating mechanisms which can be represented as mixtures of finitely many regression or autoregression models. We propose nonparametric estimators for the functions characterizing the various mixture components based on a local quasi maximum likelihood approach and prove their consistency. We present an EM algorithm for calculating the estimates numerically which is mainly based on iteratively applying common local smoothers and discuss its convergence properties.
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| Author: | Jürgen Franke, Jean-Pierre Stockis, Joseph Tadjuidje, W.K. Li |
|---|---|
| URN: | urn:nbn:de:hbz:386-kluedo-16107 |
| Series (Serial Number): | Report in Wirtschaftsmathematik (WIMA Report) (121) |
| Document Type: | Preprint |
| Language of publication: | English |
| Year of Completion: | 2009 |
| Year of first Publication: | 2009 |
| Publishing Institution: | Technische Universität Kaiserslautern |
| Creating Corporation: | Fachbereich Mathematik, University of Kaiserslautern |
| Date of the Publication (Server): | 2009/07/27 |
| Tag: | EM algorithm; hidden variables; mixture; nonparametric regression |
| Faculties / Organisational entities: | Kaiserslautern - Fachbereich Mathematik |
| DDC-Cassification: | 5 Naturwissenschaften und Mathematik / 510 Mathematik |
| MSC-Classification (mathematics): | 62-XX STATISTICS / 62Gxx Nonparametric inference / 62G08 Nonparametric regression |
| Licence (German): |  Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011 |