Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Parsimonious Dynamical Systems using the LASSO and the Bootstrap
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This project aims to investigate developments of analysis methods for time series panel data proposed by Ranganathan et al.[94]. Model selection is used as a tool for data exploration. We obtain a more stable and consistent model selection by combining stability selection [82] on the adaptive LASSO [125] with some time series bootstrapping methods [88]. The resulting method is also computationally less heavy, allowing it to handle higher dimensional and higher order models. Further, a method for validating an estimated dynamic against local polynomial gradient estimates in the data is proposed. The introduced techniques are motivated in terms of related prior research. After this, a simulation study shows that the bootstrapped  stability selection is able to identify models for some non-linear diffusion processes. Finally, the model selection method is applied to real world data previously investigated by Ranganathan et al, giving results that do not match theirs. Implications and possible extensions are discussed.

All the implemented procedures are available in packages for the R programming languages, such that one could easily continue investigating either of the introduced methods.

Place, publisher, year, edition, pages
2014.
Series
IT, 14 035
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-233042OAI: oai:DiVA.org:uu-233042DiVA: diva2:750443
Educational program
Master Programme in Computational Science; Master Programme in Mathematics
Supervisors
Examiners
Available from: 2014-09-29 Created: 2014-09-29 Last updated: 2015-04-08Bibliographically approved

Open Access in DiVA

fulltext(9846 kB)642 downloads
File information
File name FULLTEXT01.pdfFile size 9846 kBChecksum SHA-512
d85f55219a2e1a47a23302a3898eda1491d26b063a1096ee9df6a227f60ac8d969ecda143a2b15d7257d503048a7815d535824559e45e717b194415bf5fe4072
Type fulltextMimetype application/pdf

By organisation
Department of Information TechnologyDepartment of Mathematics
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 642 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 1218 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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