A robust variable screening procedure for ultra-high dimensional data
Variable selection in ultra-high dimensional regression problems has become an important issue. In such situations, penalized regression models may face computational problems and some pre screening of the variables may be necessary. A number of procedures for such pre-screening has been developed; among them the sure independence screening (SIS) enjoys some popularity. However, SIS is vulnerable to outliers in the data, and in particular in small samples this may lead to faulty inference. In this paper, we develop a new robust screening procedure. We build on the density power divergence (DPD) estimation approach and introduce DPD-SIS and its extension iterative DPD-SIS. We illustrate the behavior of the methods through extensive simulation studies and show that they are superior to both the original SIS and other robust methods when there are outliers in the data. We demonstrate the claimed robustness through use of influence functions, and we discuss appropriate choice of the tuning parameter $\alpha$. Finally, we illustrate its use on a small dataset from a study on regulation of lipid metabolism.