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Linear Discriminant Analysis with Repeated Measurements
Linköpings universitet, Matematiska institutionen, Matematisk statistik.
2019 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
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.

sted, utgiver, år, opplag, sider
2019. , s. 33
Emneord [en]
Classification, repeated measurements, linear discriminant analysis, the Growth Curve model, missing data, classification robustness, additional measurements
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-162777ISRN: LiTH-MAT-EX--2019/11--SEOAI: oai:DiVA.org:liu-162777DiVA, id: diva2:1379961
Fag / kurs
Applied Mathematics
Presentation
2019-12-03, Hopningspunkten, 10:15 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2019-12-18 Laget: 2019-12-17 Sist oppdatert: 2019-12-18bibliografisk kontrollert

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