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Particle filter-based approximate maximum likelihood inference asymptotics in state-space models
Lund University.
2007 (English)In: ESAIM: Proc. Volume 19, 2007, Conference Oxford sur les méthodes de Monte Carlo séquentielles / [ed] Andrieu, C. and Crisan, D., 2007, 115-120 p.Conference paper, Published paper (Refereed)
Abstract [en]

To implement maximum likelihood estimation in state-space models, the log-likelihoodfunction must be approximated. We study such approximations based on particle filters, and in particularconditions for consistency of the corresponding approximate maximum likelihood estimator.Numerical results illustrate the theory.

Place, publisher, year, edition, pages
2007. 115-120 p.
Series
ESAIM Proceedings, 19
Keyword [en]
Particle filter, state-space model, maximum likelihood, consistency
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-61575DOI: 10.1051/proc:071915OAI: oai:DiVA.org:kth-61575DiVA: diva2:479393
Conference
Conference Oxford sur les méthodes de Monte Carlo séquentielles
Note
QC 20120125Available from: 2012-01-17 Created: 2012-01-17 Last updated: 2012-01-25Bibliographically approved

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Rydén, Tobias
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Total: 31 hits
CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
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Output format
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