Title
Robust multivariate methods : the projection pursuit approach Robust multivariate methods : the projection pursuit approach
Author
Faculty/Department
Faculty of Sciences. Chemistry
Publication type
conferenceObject
Publication
Berlin :Springer, [*]
Subject
Computer. Automation
Source (book)
29th Annual Conference of the German Classification Society, March 9-11, 2005, Otto Guericke University Magdeburg, Magdeburg, Germany
ISBN
3-540-31313-3
ISI
000236886800032
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Projection pursuit was originally introduced to identify structures in multivariate data clouds (Huber, 1985). The idea of projecting data to a low-dimensional subspace can also be applied to multivariate statistical methods. The robustness of the methods can be achieved by applying robust estimators to the lower-dimensional space. Robust estimation in high dimensions can thus be avoided which usually results in a faster computation. Moreover, flat data sets where the number of variables is much higher than the number of observations can be easier analyzed in a robust way. We will focus on the projection pursuit approach for robust continuum regression (Serneels et al., 2005). A new algorithm is introduced and compared with the reference algorithm as well as with classical continuum regression.
E-info
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