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

被引：188

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.

引用

页码：177 / 203

页数：27

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