Interpolation Methods for Adapting to Sparse Design in Nonparametric Regression
针对局部线性平滑中的稀疏设计问题，提出基于核函数和带宽的简单插值规则，通过添加伪设计点并插值计算新纵坐标来扩充数据集，再应用局部线性平滑，该方法在简单性和性能上与岭回归等替代方法相当。
Abstract We suggest interpolation methods for overcoming the problem of sparse design in local linear smoothing. These methods are based on simple rules, determined by the kernel and bandwidth, for deciding when and where pseudo-design points should be added to augment the original design sequence. New ordinates for the added design points are computed by simple interpolation, then local linear smoothing is applied directly to the expanded dataset. The method is competitive with alternatives (e.g., those involving ridge regression), in terms of both simplicity and performance.