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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Metadaten
Author:Jürgen Franke, Jean-Pierre Stockis, Joseph Tadjuidje, W.K. Li
URN (permanent link):urn:nbn:de:hbz:386-kluedo-16107
Serie (Series number):Report in Wirtschaftsmathematik (WIMA Report) (121)
Document Type:Preprint
Language of publication:English
Year of Completion:2009
Year of Publication:2009
Publishing Institute:Technische Universität Kaiserslautern
Creating Corporation:Fachbereich Mathematik, University of Kaiserslautern
Tag:EM algorithm; hidden variables ; mixture ; nonparametric regression
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:510 Mathematik
MSC-Classification (mathematics):62G08 Nonparametric regression

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