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Robust Multivariate Methods: The Projection Pursuit Approach

  • Peter Filzmoser
  • Sven Serneels
  • Christophe Croux
  • Pierre J. Van Espen
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Partial Little Square Canonical Correlation Analysis Principal Component Regression Projection Direction Robust Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Peter Filzmoser
    • 1
  • Sven Serneels
    • 2
  • Christophe Croux
    • 3
  • Pierre J. Van Espen
    • 2
  1. 1.Department of Statistics and Probability TheoryVienna University of TechnologyViennaAustria
  2. 2.Department of ChemistryUniversity of AntwerpAntwerpBelgium
  3. 3.Department of Applied EconomicsK.U. LeuvenLeuvenBelgium

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