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Functional Hodrick-Prescott Filter
Linnaeus University, Faculty of Technology, Department of Mathematics.
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The study of functional data analysis is motivated by their applications in various fields of statistical estimation and statistical inverse problems.

In this thesis we propose a functional Hodrick-Prescott filter. This filter is applied to functional data which take values in an infinite dimensional separable Hilbert space.  The filter depends on a smoothing parameter. In this study we characterize the associated optimal smoothing parameter when the underlying distribution of the data is Gaussian. Furthermore we extend this characterization to the case when the underlying distribution of the data is white noise.

Place, publisher, year, edition, pages
Linnaeus University , 2013.
Keyword [en]
Inverse problems, adaptive estimation, Hodrick-Prescott filter, smoothing, trend extraction, Gaussian measures on a Hilbert space.
National Category
Probability Theory and Statistics
Research subject
Natural Science, Mathematics
Identifiers
URN: urn:nbn:se:lnu:diva-24233OAI: oai:DiVA.org:lnu-24233DiVA: diva2:604699
Presentation
2013-03-08, School of Computer Science, Physics and Mathematics, Växjö, 10:15 (English)
Opponent
Supervisors
Available from: 2013-02-15 Created: 2013-02-12 Last updated: 2017-02-17Bibliographically approved

Open Access in DiVA

Licentiate Thesis (Extended summary)(344 kB)111 downloads
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Type summaryMimetype application/pdf

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Nassar, Hiba

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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