Free-knot spline smoothing for functional data
成果类型：
Article
署名作者：
Gervini, Daniel
署名单位：
University of Wisconsin System; University of Wisconsin Milwaukee
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
DOI：
10.1111/j.1467-9868.2006.00561.x
发表日期：
2006
页码：
671-687
关键词：
摘要：
The paper introduces free-knot regression spline estimators for the mean and the variance components of a sample of curves. The asymptotic distribution of the mean estimator is derived, and asymptotic confidence bands are constructed. A comparative simulation study shows that free-knot splines estimate salient features of the functions (such as sharp peaks) more accurately than smoothing splines. This adaptive behaviour is also illustrated by an analysis of weather data.