Cited by 148Statistical Methods and Bayesian InferenceStatistical Methods and InferenceSurvey Sampling and Estimation Techniques

Nonparametric bootstrapping for hierarchical data

Ren, Shiquan, Lai, Hong, Tong, Wenjing, Aminzadeh, Mostafa, Hou, Xuezhang, Lai, Shenghan·2010·Journal of Applied Statistics

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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Crossref · 10.1080/02664760903046102

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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
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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