Publication
Title
Robust multivariate methods : the projection pursuit approach
Author
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.
Language
English
Source (book)
29th Annual Conference of the German Classification Society, March 9-11, 2005, Otto Guericke University Magdeburg, Magdeburg, Germany
Publication
Berlin : Springer, 2006
ISBN
3-540-31313-3
Volume/pages
p. 270-277
ISI
000236886800032
Full text (Publisher's DOI)
UAntwerpen
 Faculty/Department Research group Publication type Subject Affiliation Publications with a UAntwerp address