We present a method of variable selection for the sparse generalized additive model. The method does not assume any specific functional form, and can select from a large number of candidates. It takes the form of incremental forward stagewise regression. Given no functional form is assumed, we devised an approach termed “roughening” to adjust the residuals in the iterations. In simulations, we show the new method is competitive against popular machine learning approaches. We also demonstrate its performance using some real data sets. The method is available as a part of the nlnet package on CRAN ( https://cran.r‐project.org/package=nlnet).

中文翻译：

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具有连续结果的非线性变量选择：一种完全非参数增量式正向逐步方法。

我们提出了一种稀疏的广义加性模型的变量选择方法。该方法不假定任何特定的功能形式，并且可以从大量的候选项中进行选择。它采用增量正向逐步回归的形式。假设没有功能形式，我们设计了一种称为“粗化”的方法来调整迭代中的残差。在仿真中，我们证明了新方法与流行的机器学习方法相比具有竞争力。我们还将使用一些实际数据集来演示其性能。该方法可作为CRAN（https://cran.r‐project.org/package=nlnet）上nlnet软件包的一部分使用。