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Estimation of Missing Data for Model Validation
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
2000 (English)In: Control Systems, Preprints, Conference, TAPPI Press, 2000, 89-95 p.Conference paper, (Other academic)
Abstract [en]

To be able to validate models based on signals logged from processes, it is necessary to have a continuous data set. Since missing data is a common problem when logging process variables, it can be almost impossible to find a continuous data set. In general it might be necessary to replace missing data also when estimating models for the signals and this paper compares different approaches for missing data estimation. The result shows that on the available data set there is no significant difference in the performance and for the model validation problem it is probably enough to use the simplest method. However, for other applications and on other data sets, there are probably important differences.

Place, publisher, year, edition, pages
TAPPI Press, 2000. 89-95 p.
Keyword [en]
Missing Data, Model Validation
National Category
Mathematics
Identifiers
URN: urn:nbn:se:miun:diva-3788Scopus ID: 2-s2.0-0033717548Local ID: 4408ISBN: 1896742599 (print)OAI: oai:DiVA.org:miun-3788DiVA: diva2:28820
Conference
Control Systems 2000 'Quantifying the Benefits of Process Control'; Victoria, BC, Can; ; 1 May 2000 through 4 May 2000
Available from: 2008-09-30 Created: 2008-09-30 Last updated: 2016-10-10Bibliographically approved

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Noreus, OlofGulliksson, Mårten
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CiteExportLink to record
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Citation style
  • apa
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