  We consider the problem of screening features in an ultrahigh-dimensional
setting. Using maximum correlation, we develop a novel procedure called MC-SIS
for feature screening, and show that MC-SIS possesses the sure screen property
without imposing model or distributional assumptions on the response and
predictor variables. Therefore, MC-SIS is a model-free sure independence
screening method as in contrast with some other existing model-based sure
independence screening methods in the literature. Simulation examples and a
real data application are used to demonstrate the performance of MC-SIS as well
as to compare MC-SIS with other existing sure screening methods. The results
show that MC-SIS outperforms those methods when their model assumptions are
violated, and it remains competitive when the model assumptions hold.
