  Variable selection is of increasing importance to address the difficulties of
high dimensionality in many scientific areas. In this paper, we demonstrate a
property for distance covariance, which is incorporated in a novel feature
screening procedure together with the use of distance correlation. The approach
makes no distributional assumptions for the variables and does not require the
specification of a regression model, and hence is especially attractive in
variable selection given an enormous number of candidate attributes without
much information about the true model with the response. The method is applied
to two genetic risk problems, where issues including uncertainty of variable
selection via cross validation, subgroup of hard-to-classify cases and the
application of a reject option are discussed.
