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Linear Discriminant Analysis with Repeated Measurements
Linköping University, Department of Mathematics, Mathematical Statistics .
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The classification of observations based on repeated measurements performed on the same subject over a given period of time or under different conditions is a common procedure in many disciplines such as medicine, psychology and environmental studies. In this thesis repeated measurements follow the Growth Curve model and are classified using linear discriminant analysis. The aim of this thesis is both to examine the effect of missing data on classification accuracy and to examine the effect of additional data on classification robustness. The results indicate that an increasing amount of missing data leads to a progressive decline in classification accuracy. With regard to the effect of additional data on classification robustness the results show a less predictable effect which can only be characterised as a general tendency towards improved robustness.

Place, publisher, year, edition, pages
2019. , p. 33
Keywords [en]
Classification, repeated measurements, linear discriminant analysis, the Growth Curve model, missing data, classification robustness, additional measurements
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-162777ISRN: LiTH-MAT-EX--2019/11--SEOAI: oai:DiVA.org:liu-162777DiVA, id: diva2:1379961
Subject / course
Applied Mathematics
Presentation
2019-12-03, Hopningspunkten, 10:15 (English)
Supervisors
Examiners
Available from: 2019-12-18 Created: 2019-12-17 Last updated: 2019-12-18Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • 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