论文标题
高维计算机实验的线性筛选
Linear screening for high-dimensional computer experiments
论文作者
论文摘要
在本文中，当输入变量的数量大于运行次数时，我们为计算机实验提出了一种线性变量筛选方法。该方法使用线性模型来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。当基础模拟器几乎稀疏时，我们证明线性筛选方法在轻度条件下渐近有效。为了提高筛选精度，我们还提供了一个两阶段的过程，该过程在线性模型中使用不同的基础功能。提出的方法非常简单易用。数值结果表明，我们的方法的表现优于现有的无模型筛选方法。
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.
