The Open UniversitySkip to content
 

Generalised procrustes analysis with optimal scaling: exploring data from a power supplier

Wieringa, Jaap; Dijksterhuis, Garmt; Gower, John and van Perlo, Frederieke (2009). Generalised procrustes analysis with optimal scaling: exploring data from a power supplier. Computational Statistics and Data Analysis, 53(12) pp. 4546–4554.

Full text available as:
[img]
Preview
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (690Kb)
DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1016/j.csda.2009.03.017
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 Article
Copyright Holders: 2009 Elsevier
ISSN: 0167-9473
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Item ID: 17517
Depositing User: Colin Smith
Date Deposited: 02 Jul 2009 15:08
Last Modified: 05 Dec 2012 16:22
URI: http://oro.open.ac.uk/id/eprint/17517
Share this page:

Actions (login may be required)

View Item
Report issue / request change

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340   general-enquiries@open.ac.uk