Effect of model structure and signal-to-noise ratio on finite-time uncertainty bounding in prediction error identification
2009 (English)In: Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009, IEEE , 2009, 494-499 p.Conference paper (Refereed)
In prediction error identification, confidence regions are most commonly derived from the asymptotic statistical properties of the parameter estimator. Therefore, these confidence regions are only asymptotically valid and, for finite samples, their actual coverage rate can be smaller than the desired coverage rate. In this paper, we analyze the influence of the SNR and of the type of model structure on the difference between the actual and desired coverage rates. In addition, we propose alternatives to the classical approach to constructing probabilistic confidence regions for Box-Jenkins systems.
Place, publisher, year, edition, pages
IEEE , 2009. 494-499 p.
, Proceedings of the IEEE Conference on Decision and Control, ISSN 0191-2216
Box-Jenkins, Classical approach, Confidence region, Coverage rate, Finite samples, Parameter estimators, Prediction error identifications, Statistical properties, Time uncertainty, Asymptotic analysis, Sampling, Signal to noise ratio, Identification (control systems)
Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-152398DOI: 10.1109/CDC.2009.5400852ISI: 000336893600084ScopusID: 2-s2.0-77950793242ISBN: 978-142443871-6OAI: oai:DiVA.org:kth-152398DiVA: diva2:751004
48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009, 15 December 2009 through 18 December 2009, Shanghai, China
QC 201409302014-09-302014-09-262014-09-30Bibliographically approved