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 lowdimensional subspace can also be applied to multivariate statistical methods. The robustness of the methods can be achieved by applying robust estimators to the lowerdimensional 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 911, 2005, Otto Guericke University Magdeburg, Magdeburg, Germany  
Publication 



Berlin : Springer, 2006
 
ISBN 



3540313133
 
Volume/pages 



p. 270277
 
ISI 



000236886800032
 
Full text (Publisher's DOI) 


  
