A note on the choice of the number of slices in sliced inverse regression
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Date
2007-05-25T11:48:16Z
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Abstract
Sliced inverse regression (SIR) is a clever technique for reducing
the dimension of the predictor in regression problems, thus avoiding
the curse of dimensionality. There exist many contributions on various
aspects of the performance of SIR. Up to now, few attention has
been paid to the problem of choosing the number of slices within the
SIR procedure appropriately. The aim of this paper is to show that
especially the estimation of the reduced dimension can be strongly
influenced by the chosen number of slices.
2000 Mathematics Subject Classification: 62H12
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Keywords
Dimension reduction, Estimation of dimension