Spatially adaptive smoothing splines

• 成果类型: Article
• 署名作者: Pintore, A; Speckman, P; Holmes, CC
• 署名单位: University of Oxford; University of Missouri System; University of Missouri Columbia
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/93.1.113
• 发表日期: 2006
• 页码: 113125
• 关键词: bayesian confidence-intervals

摘要

We use a reproducing kernel Hilbert space representation to derive the smoothing spline solution when the smoothness penalty is a function lambda(t) of the design space t, thereby allowing the model to adapt to various degrees of smoothness in the structure of the data. We propose a convenient form for the smoothness penalty function and discuss computational algorithms for automatic curve fitting using a generalised crossvalidation measure.