Robust penalized regression spline fitting with application to additive mixed modeling

被引：0
|
作者：
Thomas C. M. Lee
Hee-Seok Oh
机构：
[1] Colorado State University,Department of Statistics
[2] Seoul National University,Department of Statistics
来源：
Computational Statistics | 2007年 / 22卷
关键词：
Additive mixed models; M-type robust estimation; Penalized splines; Robust smoothing; Semiparametric regression;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
An increasingly popular method for smoothing noisy data is penalized regression spline fitting. In this paper a new procedure is proposed for fitting robust penalized regression splines. This procedure is computationally fast, straightforward to implement, and can be paired with any smoothing parameter selection method. In addition, it can also be extended to other settings, such as additive mixed modeling. Both simulated and real data examples are used to illustrate the effectiveness of the procedure.
引用
收藏
页码：159 / 171
页数：12
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