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Linear grouping using orthogonal regression

Linear grouping using orthogonal regression

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Title: Linear grouping using orthogonal regression
Author: Aelst, Stefan Van
Wang, Xiaogang
Zamar, Ruben H.
Zhu, Rong
Abstract: A new method to detect different linear structures in a data set, called Linear Grouping Algorithm
(LGA), is proposed. LGA is useful for investigating potential linear patterns in data sets, that is,
subsets that follow different linear relationships. LGA combines ideas from principal components,
clustering methods and resampling algorithms. It can detect several different 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 applications illustrate the usefulness
of LGA in practice.
Subject: Linear grouping
Orthogonal regression
Type: Article
URI: http://hdl.handle.net/10315/923
Published: Computational Statistics and Data Analysis
Series: 50 ; 5
Citation: van Alest, S., Wang, X., Zamar, R.H. and Zhu,R. (2006). Linear Grouping Using Orthogonal Regression. Computational Statistics and Data Analysis. Vol. 50, No. 5, 1287-1312.
ISSN: 0167-9473
Date: 2006

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