Return to search

Linear Discriminant Analysis with Repeated Measurements

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.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-162777
Date January 2019
CreatorsSkinner, Evelina
PublisherLinköpings universitet, Matematisk statistik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

Page generated in 0.0021 seconds