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L2 Model reduction and Variance Reduction - Extended Version
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2000 (English)Report (Other academic)
Abstract [en]

In this contribution we demonstrate that estimating a low order model (leaving some dynamics unmodeled) by L2 model reduction of a higher order estimated model may give smaller variance and mean square error than directly estimating it from the same data that produced the high order model. It will also be shown in a quite general case that the reduced model will reach the Cramer-Rao bound if no under modeling is present. From the derivations of this result it follows that L2model reduction is optimal, meaning that the reduced model possesses the lowest possible variance.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2000. , 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2310
Keyword [en]
Model reduction, Identification, Variance reduction
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55761ISRN: LiTH-ISY-R-2310OAI: oai:DiVA.org:liu-55761DiVA: diva2:316572
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-03Bibliographically approved

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Ljung, Lennart

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf