An online approach to detecting changes in nonlinear autoregressive models

  • In this paper we develop monitoring schemes for detecting structural changes in nonlinear autoregressive models. We approximate the regression function by a single layer feedforward neural network. We show that CUSUM-type tests based on cumulative sums of estimated residuals, that have been intensively studied for linear regression in both an offline as well as online setting, can be extended to this model. The proposed monitoring schemes reject (asymptotically) the null hypothesis only with a given probability but will detect a large class of alternatives with probability one. In order to construct these sequential size tests the limit distribution under the null hypothesis is obtained.

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Metadaten
Author:Claudia Kirch, Joseph Tadjuidje Kamgaing
URN (permanent link):urn:nbn:de:hbz:386-kluedo-27725
Serie (Series number):Report in Wirtschaftsmathematik (WIMA Report) (142)
Document Type:Report
Language of publication:English
Publication Date:2011/10/11
Year of Publication:2011
Publishing Institute:Technische Universität Kaiserslautern
Tag:autoregressive process; change analysis; neural network; nonparametric regression; sequential test
Number of page:14
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:519 Wahrscheinlichkeiten, angewandte Mathematik

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