A uniform central limit theorem for neural network based autoregressive processes with applications to change-point analysis

  • We consider an autoregressive process with a nonlinear regression function that is modeled by a feedforward neural network. We derive a uniform central limit theorem which is useful in the context of change-point analysis. We propose a test for a change in the autoregression function which - by the uniform central limit theorem - has asymptotic power one for a large class of alternatives including local alternatives.

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Metadaten
Author:Claudia Kirch, Joseph Tadjuidje Kamgaing
URN (permanent link):urn:nbn:de:hbz:386-kluedo-16921
Serie (Series number):Report in Wirtschaftsmathematik (WIMA Report) (138)
Document Type:Preprint
Language of publication:English
Year of Completion:2011
Year of Publication:2011
Publishing Institute:Technische Universität Kaiserslautern
Tag:autoregressive process; neural network ; nonparametric regression ; uniform central limit theorem
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:510 Mathematik
MSC-Classification (mathematics):60F05 Central limit and other weak theorems
62J02 General nonlinear regression
62M45 Neural nets and related approaches

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