Wieringa, Jaap; Dijksterhuis, Garmt; Gower, John and van Perlo, Frederieke
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|DOI (Digital Object Identifier) Link:||https://doi.org/10.1016/j.csda.2009.03.017|
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Generalised Procrustes Analysis (GPA) is a method for matching several, possibly large, data sets by fitting them to each other using transformations, typically rotations. The linear version of GPA has been applied in a wide range of contexts. A non-linear extension of GPA is developed which uses Optimal Scaling (OS). The approach is suited to match data sets that contain nominal variables. A database of a Dutch power supplier that contains many categorical variables unfit for the usual linear GPA methodology is used to illustrate the approach.
|Item Type:||Journal Article|
|Copyright Holders:||2009 Elsevier|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Colin Smith|
|Date Deposited:||02 Jul 2009 15:08|
|Last Modified:||06 Oct 2016 18:26|
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