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Maximum Likelihood Estimation of Gaussian Models with Missing Data: Eight Equivalent Formulations
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.
2012 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 48, no 9, 1955-1962 p.Article in journal (Refereed) Published
Abstract [en]

In this paper we derive the maximum likelihood problem for missing data from a Gaussian model. We present in total eight different equivalent formulations of the resulting optimization problem, four out of which are nonlinear least squares formulations. Among these formulations are also formulations based on the expectation-maximization algorithm. Expressions for the derivatives needed in order to solve the optimization problems are presented. We also present numerical comparisons for two of the formulations for an ARMAX model.

Place, publisher, year, edition, pages
Elsevier, 2012. Vol. 48, no 9, 1955-1962 p.
Keyword [en]
Maximum likelihood estimation, Missing data, Expectation-maximization algorithm, ARMAX models
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-84538DOI: 10.1016/j.automatica.2012.05.060ISI: 000308454100002OAI: oai:DiVA.org:liu-84538DiVA: diva2:560163
Available from: 2012-10-12 Created: 2012-10-12 Last updated: 2017-12-07

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Hansson, AndersWallin, Ragnar

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CiteExportLink to record
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Cite
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