A simple root n bandwidth selector for nonparametric regression

被引:5
|
作者
Heiler, S [1 ]
Feng, YH [1 ]
机构
[1] Univ Konstanz, Dept Econ & Stat, D-78434 Constance, Germany
关键词
bandwidth choice; double-smoothing; plug-in; local linear regression;
D O I
10.1080/10485259808832733
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
The purpose of this paper is to investigate data-driven bandwidth selection for nonparametric regression based on a double-smoothing procedure. It will be shown that the best convergence rate can be achieved by kernel regression with non-negative kernels in both pilot smoothing and as well as in main smoothing. The asymptotic results are given for a naive kernel estimator with an equally spaced design, but they can also be used for other kernel estimators or for locally weighted regression. Three variates of data-driven bandwidth selectors for local linear regression are proposed. One of them, (h) over cap(DS1), is root n consistent. The performance of these bandwidth selectors is studied through simulation. They are also compared with the bandwidths selected by the R criterion of Rice and the true ASE optimal bandwidth (h(ASE)) In spite of satisfactory performances of all bandwidth selectors, the root n one turns out to be the best in theory as well as in practice.
引用
收藏
页码:1 / 21
页数:21
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