Mixtures of Nonparametric Autoregressions

  • 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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Metadaten
Author:Jürgen Franke, Jean-Pierre Stockis, Joseph Tadjuidje, W.K. Li
URN:urn:nbn:de:hbz:386-kluedo-16082
Series (Serial Number):Report in Wirtschaftsmathematik (WIMA Report) (120)
Document Type:Preprint
Language of publication:English
Year of Completion:2009
Year of first Publication:2009
Publishing Institution:Technische Universität Kaiserslautern
Creating Corporation:University of Kaiserslautern
Date of the Publication (Server):2009/07/13
Tag:EM algorith; hidden variables; kernel estimates; mixture; nonparametric regression
Faculties / Organisational entities:Kaiserslautern - Fachbereich Mathematik
DDC-Cassification:5 Naturwissenschaften und Mathematik / 510 Mathematik
Licence (German):Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011