Nonparametric bootstrapping for hierarchical data
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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Combining multilevel models (MLMs) with cluster bootstrapping is recommended for resampling clustered data.
“MLMs will be combined with cluster bootstrapping, which is recommended for resampling clustered data (103).”
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- Authors
- Ren, S., Lai, H., Tong, W., Aminzadeh, M., Hou, X., & Lai, S.
- Affiliations
- UNSW Sydney (AU)
- Johns Hopkins University (US)
- Towson University (US)
- Corresponding author
- Ren, Shiquan
- Venue
- Journal of Applied Statistics
- Publisher
- Informa UK Limited
- Published
- 2010-09
- Volume
- 37
- Issue
- 9
- Pages
- 1487-1498
- ISSN
- 0266-4763 (ISSN-L)
- 1360-0532
- OpenAlex
- W2084577255
- Cited by
- 148
- References
- 16
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 20 · 2023: 19 · 2022: 10 · 2021: 17 · 2020: 12 · 2019: 13 · 2018: 8 · 2017: 6 · 2016: 8 · 2015: 8 · 2014: 6 · 2013: 1 · 2012: 3
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Applied Statistics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Keywords
- nonparametric bootstrap, hierarchical data, resampling strategies, multilevel data, cluster sampling, simulation study
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