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Connections Between Optimisation-Based Regressor Selection and Analysis of Variance
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.
2006 (English)Report (Other academic)
Abstract [en]

Earlier contributions have shown that Analysis of Variance (ANOVA) can be successfully used for finding good regressors for nonlinear models in a nonlinear black-box system identification context. In this paper, it is shown that the ANOVA problem can be recast as an optimisation problem. Two modified, convex versions of the ANOVA optimisation problem are then proposed, and it turns out that they are closely related to the nn-garrote and wavelet shrinkage methods, respectively. In the case of balanced data, it is also shown that the methods have a nice orthogonality property in the sense that different groups of parameters can be computed independently.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2006. , 24 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2728
Keyword [en]
ANOVA, 1-norm, Lasso, Nn-garrote, Nonlinear identification, Regressor selection
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-56098ISRN: LiTH-ISY-R-2728OAI: oai:DiVA.org:liu-56098DiVA: diva2:316835
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-09-19Bibliographically approved

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Roll, JacobLind, Ingela

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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