Title: Linear grouping using orthogonal regression
Authors: Van Aelst, Stefan ×
Wang, XG
Zamar, RH
Zhu, R #
Issue Date: 2006
Series Title: COMPUTATIONAL STATISTICS & DATA ANALYSIS vol:50 issue:5 pages:1287-1312
Abstract: A new method to detect diÆerent linear structures in a data set, called linear group-
ing algorithm (LGA), is proposed. LGA is useful for investigating potential linear
patterns in datasets, that is, subsets that follow diÆerent linear relationships. LGA
combines ideas from principal components, clustering methods and resampling algo-
rithms. It can detect several diÆerent linear relations at once. Methods to determine
the number of groups in the data are proposed. Diagnostic tools to investigate the
results obtained from LGA are introduced. It is shown how LGA can be extended
to detect groups characterized by lower dimensional hyperplanes as well. Some ap-
plications illustrate the usefulness of LGA in practice.
ISSN: 0167-9473
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

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