Nonparametric bootstrapping for hierarchical data

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...

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Bibliographic Details
Published in:Journal of applied statistics 2010-09, Vol.37 (9), p.1487-1498
Main Authors: Ren, Shiquan, Lai, Hong, Tong, Wenjing, Aminzadeh, Mostafa, Hou, Xuezhang, Lai, Shenghan
Format: Article
Language:English
Subjects:
Bootstrap method
hierarchical data
nonparametric bootstrapping
random effects model
Random number sampling
resampling schemes
Simulation
Studies
unbalanced data
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Summary: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.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664760903046102