Abstract
Since the sure independence screening (SIS)
method by Fan and Lv [2008], many different
variable screening methods have been proposed
based on different measures under different models. However, most of these methods are designed
for specific models. In practice, we often have very
little information about the data generating process
and different methods can result in very different
sets of features. The heterogeneity presented here
motivates us to combine various screening methods simultaneously. In this paper, we introduce
a general ensemble-based framework to efficiently
combine results from multiple variable screening
methods. The consistency and sure screening
property of proposed framework has been established. Extensive simulation studies confirm our intuition that the proposed ensemble-based method is
more robust against model specification than using
single variable screening method. The proposed
ensemble-based method is used to predict attention
deficit hyperactivity disorder (ADHD) status using
brain function connectivity (FC)