Nonparametric bootstrapping for hierarchical data |
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| Authors: | Shiquan Ren Hong Lai Wenjing Tong Mostafa Aminzadeh Xuezhang Hou Shenghan Lai |
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| Affiliation: | 1. School of Biological, Earth and Environmental Sciences , University of New South Wales , Sydney , Australia;2. Departments of Radiology and Pathology , Johns Hopkins University School of Medicine , Baltimore , USA;3. Mathematics Department of Towson University , Towson , USA |
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| Abstract: | 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. |
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| Keywords: | random effects model hierarchical data nonparametric bootstrapping resampling schemes unbalanced data |
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