Wieringa, Jaap; Dijksterhuis, Garmt; Gower, John and van Perlo, Frederieke
(2009).
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| DOI (Digital Object Identifier) Link: | https://doi.org/10.1016/j.csda.2009.03.017 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
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 Item |
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| Copyright Holders: | 2009 Elsevier |
| ISSN: | 0167-9473 |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Item ID: | 17517 |
| Depositing User: | Colin Smith |
| Date Deposited: | 02 Jul 2009 15:08 |
| Last Modified: | 11 Jun 2018 16:38 |
| URI: | http://oro.open.ac.uk/id/eprint/17517 |
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