Linear grouping using orthogonal regression

dc.contributor.authorVan Aelst, Stefan
dc.contributor.authorWang, Xiaogang
dc.contributor.authorZamar, Ruben H.
dc.contributor.authorZhu, Rong
dc.date.accessioned2007-03-27T18:43:08Z
dc.date.available2007-03-27T18:43:08Z
dc.date.issued2006
dc.description.abstractA 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.
dc.identifier.citationvan 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.
dc.identifier.issn0167-9473
dc.identifier.urihttp://hdl.handle.net/10315/923
dc.identifier.urihttps://doi.org/10.1016/j.csda.2004.11.011
dc.language.isoen
dc.publisherComputational Statistics and Data Analysis
dc.rights© 2006. This manuscript version is made available under the CC-BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectLinear grouping
dc.subjectOrthogonal regression
dc.titleLinear grouping using orthogonal regression
dc.typeArticle

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