Title: Inference for robust canonical variate analysis
Authors: Van Aelst, Stefan ×
Willems, Gert #
Issue Date: 2010
Series Title: ADVANCES IN DATA ANALYSIS AND CLASSIFICATION vol:4 issue:2-3 pages:181-197
Abstract: We consider the problem of optimally separating two multivariate
populations. Robust linear discriminant rules can be obtained by replacing
the empirical means and covariance in the classical discriminant rules by S or
MM-estimates of location and scatter. We propose to use a fast and robust
bootstrap method to obtain inference for such a robust discriminant analysis.
This is useful since classical bootstrap methods may be unstable as well as
extremely time-consuming when robust estimates such as S or MM-estimates
are involved. In particular, fast and robust bootstrap can be used to investigate
which variables contribute signicantly to the canonical variate, and thus the
discrimination of the classes. Through bootstrap, we can also examine the
stability of the canonical variate. We illustrate the method on some real data
ISSN: 1862-5347
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

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