This paper is concerned with screening features in ultrahigh dimensional
data analysis, which has become increasingly  important in diverse
scientific fields. We develop a sure independence screening procedure
based on the distance correlation (DC-SIS, for short). The DC-SIS can be
implemented as easily as the sure independence screening procedure based
on the Pearson correlation (SIS, for short)proposed by Fan and Lv (2008).
However, the DC-SIS can significantly improve the SIS. Fan and Lv (2008)
established the sure screening property for the SIS based on linear
models, but the sure screening property is valid for the DC-SIS under more
general settings including linear models. Furthermore, the implementation
of the DC-SIS does not require model specification (e.g., linear model or
generalized linear model) for responses or predictors. This is a very
appealing property in ultrahigh dimensional data analysis. Moreover, the
DC-SIS can be used directly to screen grouped predictor variables and for
multivariate response variables. We establish the sure screening property
for the DC-SIS, and conduct simulations to examine its finite sample
performance. Numerical comparison indicates that the DC-SIS performs much
better than the SIS in various models. We also illustrate the DC-SIS
through a real data example.