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.
Verfasser*innenangaben: | Claudia Kirch, Joseph Tadjuidje Kamgaing |
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URN: | urn:nbn:de:hbz:386-kluedo-27725 |
Schriftenreihe (Bandnummer): | Report in Wirtschaftsmathematik (WIMA Report) (142) |
Dokumentart: | Bericht |
Sprache der Veröffentlichung: | Englisch |
Datum der Veröffentlichung (online): | 11.10.2011 |
Jahr der Erstveröffentlichung: | 2011 |
Veröffentlichende Institution: | Technische Universität Kaiserslautern |
Datum der Publikation (Server): | 24.10.2011 |
Freies Schlagwort / Tag: | autoregressive process; change analysis; neural network; nonparametric regression; sequential test |
Seitenzahl: | 14 |
Fachbereiche / Organisatorische Einheiten: | Kaiserslautern - Fachbereich Mathematik |
DDC-Sachgruppen: | 5 Naturwissenschaften und Mathematik / 510 Mathematik |
Lizenz (Deutsch): | Standard gemäß KLUEDO-Leitlinien vom 27.05.2011 |