NONPARAMETRIC REGRESSION - OPTIMAL LOCAL BANDWIDTH CHOICE

被引：0
|
作者：
VIEU, P
机构：
来源：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL | 1991年 / 53卷 / 02期
关键词：
KERNEL ESTIMATES; LOCAL BANDWIDTH SELECTION RULE; MEDICAL DATA; MONTE-CARLO METHOD; NONPARAMETRIC REGRESSION;
D O I：
暂无
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：
Kernel estimators of a regression function are investigated. The bandwidths are locally chosen by a data-driven method based on the minimization of a local cross-validation criterion. This method is shown to be asymptotically optimal with respect to local quadratic measures of errors. Monte Carlo experiments are presented, and finally the method is applied to some data of medical interest.
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页码：453 / 464
页数：12
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