  Regularized linear models, such as Lasso, have attracted great attention in
statistical learning and data science. However, there is sporadic work on
constructing efficient data collection for regularized linear models. In this
work, we propose an experimental design approach, using nearly orthogonal Latin
hypercube designs, to enhance the variable selection accuracy of the
regularized linear models. Systematic methods for constructing such designs are
presented. The effectiveness of the proposed method is illustrated with several
examples.
