A comparison of regression spline smoothing procedures

被引：64
|
作者：
Wand, MP [1 ]
机构：
[1] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
来源：
COMPUTATIONAL STATISTICS | 2000年 / 15卷 / 04期
关键词：
Bayesian variable selection; B-spline; Gibbs sampling; nonparametric regression; polynomial spline; roughness penalty; stepwise regression;
D O I：
10.1007/s001800000047
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Regression spline smoothing involves modelling a regression function as a piecewise polynomial with a high number of pieces relative to the sample size. Because the number of possible models is so large, efficient strategies for choosing among them are required. In this paper we review approaches to this problem and compare them through a simulation study. For simplicity and conciseness we restrict attention to the univariate smoothing setting with Gaussian noise and the truncated polynomial regression spline basis.
引用
收藏
页码：443 / 462
页数：20
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