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 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 for some extreme cases, 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.

中文翻译：

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高维计算机实验的线性筛选

在本文中，当输入变量的数量大于运行次数时，我们提出了一种用于计算机实验的线性变量筛选方法。该方法使用线性模型对非线性数据进行建模，并通过现有的线性模型筛选方法筛选重要变量。当底层模拟器几乎稀疏时，我们证明了线性筛选方法在温和条件下是渐近有效的。为了提高某些极端情况下的筛选精度，我们还提供了一个在线性模型中使用不同基函数的两步过程。所提出的方法非常简单并且易于实现。数值结果表明，我们的方法优于现有的无模型筛选方法。