Nonparametric regression estimators in complex surveys

被引:4
|
作者
Zhang, Guoyi [1 ]
Christensen, Fletcher [2 ]
Zheng, Wei [3 ]
机构
[1] Univ New Mexico, Dept Math & Stat, Albuquerque, NM 87131 USA
[2] Univ Calif Irvine, Dept Stat, Irvine, CA 92697 USA
[3] Indiana Univ Purdue Univ, Dept Math, Indianapolis, IN 46202 USA
关键词
smoothing splines; local liner estimator; simulations; complex surveys; nonparametric regression; DENSITY-ESTIMATION;
D O I
10.1080/00949655.2013.860139
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this article, we extend smoothing splines to model the regression mean structure when data are sampled through a complex survey. Smoothing splines are evaluated both with and without sample weights, and are compared with local linear estimator. Simulation studies find that nonparametric estimators perform better when sample weights are incorporated, rather than being treated as if iid. They also find that smoothing splines perform better than local linear estimator through completely data-driven bandwidth selection methods.
引用
收藏
页码:1026 / 1034
页数:9
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