Fast Robust Variable Selection

Abstract

We discuss some computationally efficient procedures for robust variable selection in linear regression. A key component in these procedures is the computation of robust correlations between pairs of variables. We show that the robust variable selection procedures can easily handle missing data under the assumption that data are missing completely at random.