  In this paper we propose a linear variable screening method for computer
experiments when the number of input variables is larger than the number of
runs. This method uses a linear model to model the nonlinear data, and screens
the important variables by existing screening methods for linear models. When
the underlying simulator is nearly sparse, we prove that the linear screening
method is asymptotically valid under mild conditions. To improve the screening
accuracy, we also provide a two-stage procedure that uses different basis
functions in the linear model. The proposed methods are very simple and easy to
implement. Numerical results indicate that our methods outperform existing
model-free screening methods.
