The estimating function bootstrap

被引:51
作者
Hu, FF [1 ]
Kalbfleisch, JD [1 ]
机构
[1] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 119260, Singapore
来源
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2000年 / 28卷 / 03期
关键词
bootstrap; common means problem; confidence intervals; estimating equation; estimating function; generalized score; linear regression; nonlinear regression; resampling; significance tests;
D O I
10.2307/3315958
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 authors propose a bootstrap procedure which estimates the distribution of an estimating function by resampling its terms using bootstrap techniques. Studentized versions of this so-called estimating function (EF) bootstrap yield methods which are invariant under reparametrizations. This approach often has substantial advantage, both in computation and accuracy, over more traditional bootstrap methods and it applies to a wide class of practical problems where the data are independent but not necessarily identically distributed. The methods allow for simultaneous estimation of vector parameters and their components. The authors use simulations to compare the EF bootstrap with competing methods in several examples including the common means problem and nonlinear regression. They also prove symptotic results showing that the studentized EF bootstrap yields higher order approximations for the whole vector parameter in a wide class of problems.
引用
收藏
页码:449 / 481
页数:33
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