TY - JOUR
T1 - Generalised procrustes analysis with optimal scaling
T2 - Exploring data from a power supplier
AU - Wieringa, J.E.
AU - Dijksterhuis, G.B.
AU - Gower, J.C.
AU - van Perlo-ten Kleij, F.
PY - 2009/10/1
Y1 - 2009/10/1
N2 - 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. (c) 2009 Elsevier B.V. All rights reserved.
AB - 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. (c) 2009 Elsevier B.V. All rights reserved.
U2 - 10.1016/j.csda.2009.03.017
DO - 10.1016/j.csda.2009.03.017
M3 - Article
SN - 0167-9473
VL - 53
SP - 4546
EP - 4554
JO - Computational Statistics & Data Analysis
JF - Computational Statistics & Data Analysis
IS - 12
ER -