A robust linear grouping algorithm

Greet Pison (), Stefan Van Aelst () and Ruben H. Zamar ()
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Greet Pison: Ghent University (UGent), Department of Applied Mathematics and Computer Science
Stefan Van Aelst: Ghent University (UGent), Department of Applied Mathematics and Computer Science
Ruben H. Zamar: University of British Columbia, Department of Statistics

A chapter in Compstat 2006 - Proceedings in Computational Statistics, 2006, pp 43-53 from Springer

Abstract: Abstract Recently, an algorithm to detect groups in a dataset that follow different linear patterns was proposed in [VWZ06]. The algorithm is flexible in the sense that it does not require the specification of a response variable. On the other hand, the algorithm requires that each observation follows one of the linear patterns in the data. However, it often occurs in practice that part of the data does not follow any of the linear patterns. Therefore, we introduce a robust linear grouping algorithm based on trimming that can still find the linear structures even if part of the data does not belong to any of the groups.

Keywords: Linear grouping; robustness; trimming (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1709-6_4

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DOI: 10.1007/978-3-7908-1709-6_4

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