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Title
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Robust multivariate methods : the projection pursuit approach
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Author
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Abstract
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| 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. |
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Language
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English
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Source (book)
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29th Annual Conference of the German Classification Society, March 9-11, 2005, Otto Guericke University Magdeburg, Magdeburg, Germany
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Publication
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Berlin
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Springer
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2006
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ISBN
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3-540-31313-3
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DOI
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10.1007/3-540-31314-1_32
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Volume/pages
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p. 270-277
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ISI
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000236886800032
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Full text (Publisher's DOI)
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