We deal with the problem of feature selection for high-dimensional additive models in this article. The existing feature selection methods for additive models in the literature mainly concentrate on the penalized likelihood approach. We propose a sequential group selection method for additive models (sgsAM) in this article. The additive functions are estimated by B-spline method and are treated as groups of features. The groups are selected sequentially by a correlation search procedure. It is shown that the sgsAM method is selection consistent. Numerical studies using simulated data and a real data demonstrate that the sgsAM method has an advantage over the existing methods.

在本文中，我们处理高维加性模型的特征选择问题。文献中现有的用于加性模型的特征选择方法主要集中在惩罚似然法上。我们在本文中提出了用于加性模型（sgsAM）的顺序组选择方法。可加函数通过B样条法估计，并被视为特征组。通过相关性搜索过程依次选择组。结果表明，sgsAM方法是选择一致的。使用模拟数据和实际数据进行的数值研究表明，sgsAM方法比现有方法具有优势。