  In this article, we introduce a procedure for selecting variables in
principal components analysis. The procedure was developed to identify a small
subset of the original variables that best explain the principal components
through nonparametric relationships. There are usually some noisy uninformative
variables in a dataset, and some variables that are strongly related to each
other because of their general interdependence. The procedure is designed to be
used following the satisfactory initial use of a principal components analysis
with all variables, and its aim is to help to interpret underlying structures.
We analyze the asymptotic behavior of the method and provide some examples.
