USING THE BOOTSTRAP TO ESTIMATE MEAN SQUARED ERROR AND SELECT SMOOTHING PARAMETER IN NONPARAMETRIC PROBLEMS

被引：191
作者：
HALL, P
机构：
[1] Australian National University, Canberra
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 1990年 / 32卷 / 02期
关键词：
bias; bootstrap; density estimation; mean squared error; nonparametric regression; smoothing parameter;
D O I：
10.1016/0047-259X(90)90080-2
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：
We describe a bootstrap method for estimating mean squared error and smoothing parameter in nonparametric problems. The method involves using a resample of smaller size than the original sample. There are many applications, which are illustrated using the special cases of nonparametric density estimation, nonparametric regression, and tail parameter estimation. © 1990.
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页码：177 / 203
页数：27
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