A NEW CLASS OF KERNELS FOR NONPARAMETRIC CURVE ESTIMATION

• 成果类型: Article
• 署名作者: MESSER, K; GOLDSTEIN, L
• 署名单位: University of Southern California
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176349021
• 发表日期: 1993
• 页码: 179-195
• 关键词: regression

摘要

We introduce a new class of variable kernels which depend on the smoothing parameter b through a simple scaling operation, and which have good integrated mean square error (IMSE) convergence properties. These kernels deform ''automatically'' near the boundary, eliminating boundary bias. Computational formulas are given for all orders of kernel in terms of exponentially damped sines and cosines. The kernel is a computationally convenient approximation to a certain Green's function, with the resulting kernel estimate closely related to a smoothing spline estimate.