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.

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DOI (Digital Object Identifier) Link:	http://dx.doi.org/10.1016/j.csda.2009.03.017

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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

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