Nonparametric bootstrapping for hierarchical data

被引：109

作者：

Ren, Shiquan ^{[1
]}

Lai, Hong ^{[2
,3
]}

Tong, Wenjing ^{[2
,3
]}

Aminzadeh, Mostafa ^{[4
]}

Hou, Xuezhang ^{[4
]}

Lai, Shenghan ^{[2
,3
]}

机构：

[1] Univ New S Wales, Sch Biol Earth & Environm Sci, Sydney, NSW, Australia

[2] Johns Hopkins Univ, Sch Med, Dept Radiol, Baltimore, MD 21205 USA

[3] Johns Hopkins Univ, Sch Med, Dept Pathol, Baltimore, MD 21205 USA

[4] Towson Univ, Dept Math, Towson, MD USA

来源：

JOURNAL OF APPLIED STATISTICS | 2010年 / 37卷 / 09期

关键词：

random effects model; hierarchical data; nonparametric bootstrapping; resampling schemes; unbalanced data;

D O I：

10.1080/02664760903046102

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：

Nonparametric bootstrapping for hierarchical data is relatively underdeveloped and not straightforward: certainly it does not make sense to use simple nonparametric resampling, which treats all observations as independent. We have provided some resampling strategies of hierarchical data, proved that the strategy of nonparametric bootstrapping on the highest level (randomly sampling all other levels without replacement within the highest level selected by randomly sampling the highest levels with replacement) is better than that on lower levels, analyzed real data and performed simulation studies.

引用

页码：1487 / 1498

页数：12

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